Depth maps paired with their source photograph

Monocular depth maps rendered at their plate's own resolution, so depth and colour line up pixel for pixel and a consumer never has to resample before comparing. Useful for testing depth-aware compositing, relighting and 3D reconstruction inputs.

120 of 120 files

A av f3: depth map

A depth map for the published plate a-av-f3.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av f4: depth map

A depth map for the published plate a-av-f4.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av f5: depth map

A depth map for the published plate a-av-f5.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m3: depth map

A depth map for the published plate a-av-m3.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m4: depth map

A depth map for the published plate a-av-m4.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m5: depth map

A depth map for the published plate a-av-m5.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av m6: depth map

A depth map for the published plate a-av-m6.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

A av nb1: depth map

A depth map for the published plate a-av-nb1.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

png

179.2 KB

A avatar f1: depth map

A depth map for the published plate a-avatar-f1.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

png

160.1 KB

A avatar f2: depth map

A depth map for the published plate a-avatar-f2.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 92.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

png

125 KB

A avatar m1: depth map

A depth map for the published plate a-avatar-m1.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 80.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

png

145.6 KB

A avatar m2: depth map

A depth map for the published plate a-avatar-m2.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

C album abs: depth map

A depth map for the published plate c-album-abs.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

C album moody: depth map

A depth map for the published plate c-album-moody.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 38.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

C album retro: depth map

A depth map for the published plate c-album-retro.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

C book hist: depth map

A depth map for the published plate c-book-hist.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

C book nature: depth map

A depth map for the published plate c-book-nature.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 81.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

C book thrill: depth map

A depth map for the published plate c-book-thrill.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 65.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

D clipboard: depth map

A depth map for the published plate d-clipboard.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

D invoice: depth map

A depth map for the published plate d-invoice.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

D ledger: depth map

A depth map for the published plate d-ledger.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

D menu: depth map

A depth map for the published plate d-menu.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 74.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

D receipts: depth map

A depth map for the published plate d-receipts.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

F bread rustic: depth map

A depth map for the published plate f-bread-rustic.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 82.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

F coffee pour: depth map

A depth map for the published plate f-coffee-pour.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 75.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

F plate pasta: depth map

A depth map for the published plate f-plate-pasta.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

F plate salad: depth map

A depth map for the published plate f-plate-salad.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 90.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G char knight: depth map

A depth map for the published plate g-char-knight.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G char mage: depth map

A depth map for the published plate g-char-mage.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 78.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G char scifi: depth map

A depth map for the published plate g-char-scifi.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G dungeon: depth map

A depth map for the published plate g-dungeon.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 91.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G fantasy keep: depth map

A depth map for the published plate g-fantasy-keep.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 64.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G isometric: depth map

A depth map for the published plate g-isometric.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G lowpoly isle: depth map

A depth map for the published plate g-lowpoly-isle.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G pixel scene: depth map

A depth map for the published plate g-pixel-scene.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G platformer: depth map

A depth map for the published plate g-platformer.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 77.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G racing track: depth map

A depth map for the published plate g-racing-track.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 90.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G scifi corr: depth map

A depth map for the published plate g-scifi-corr.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

G voxel city: depth map

A depth map for the published plate g-voxel-city.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Invoice desk: depth map

A depth map for the published plate invoice-desk.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

L bridge: depth map

A depth map for the published plate l-bridge.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 80.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L city sky: depth map

A depth map for the published plate l-city-sky.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 66.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L coast: depth map

A depth map for the published plate l-coast.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 63.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L forest path: depth map

A depth map for the published plate l-forest-path.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L interior mod: depth map

A depth map for the published plate l-interior-mod.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 84.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L mountain: depth map

A depth map for the published plate l-mountain.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 69.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L stairwell: depth map

A depth map for the published plate l-stairwell.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

L street eu: depth map

A depth map for the published plate l-street-eu.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Ledger book: depth map

A depth map for the published plate ledger-book.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Meeting table: depth map

A depth map for the published plate meeting-table.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 88.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Menu table: depth map

A depth map for the published plate menu-table.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 92.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

N birds feed: depth map

A depth map for the published plate n-birds-feed.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 52.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

N cat window: depth map

A depth map for the published plate n-cat-window.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

N dog park: depth map

A depth map for the published plate n-dog-park.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 72.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

N fish tank: depth map

A depth map for the published plate n-fish-tank.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 76.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

N horse field: depth map

A depth map for the published plate n-horse-field.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 72.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

N sheep hill: depth map

A depth map for the published plate n-sheep-hill.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 91.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Office desk: depth map

A depth map for the published plate office-desk.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Owner cafe: depth map

A depth map for the published plate owner-cafe.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 73.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

Owner market: depth map

A depth map for the published plate owner-market.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 89.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Owner restaurant: depth map

A depth map for the published plate owner-restaurant.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

Owner salon: depth map

A depth map for the published plate owner-salon.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 65.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

Owner shop: depth map

A depth map for the published plate owner-shop.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 90.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

Owner trades: depth map

A depth map for the published plate owner-trades.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

P accountant: depth map

A depth map for the published plate p-accountant.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 85.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P baker young: depth map

A depth map for the published plate p-baker-young.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P barista deaf: depth map

A depth map for the published plate p-barista-deaf.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P butcher mid: depth map

A depth map for the published plate p-butcher-mid.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 71.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P chef wheel: depth map

A depth map for the published plate p-chef-wheel.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 89.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P cleaner eve: depth map

A depth map for the published plate p-cleaner-eve.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P desk chair: depth map

A depth map for the published plate p-desk-chair.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 80.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

P driver eve: depth map

A depth map for the published plate p-driver-eve.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

P farmer field: depth map

A depth map for the published plate p-farmer-field.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 71.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P florist sen: depth map

A depth map for the published plate p-florist-sen.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 90.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P kitchen chef: depth map

A depth map for the published plate p-kitchen-chef.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 85.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

P market sen: depth map

A depth map for the published plate p-market-sen.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 88.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

P mechanic w: depth map

A depth map for the published plate p-mechanic-w.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P meeting mix: depth map

A depth map for the published plate p-meeting-mix.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 91.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

P nurse clinic: depth map

A depth map for the published plate p-nurse-clinic.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 82.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P salon mid: depth map

A depth map for the published plate p-salon-mid.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

P shop plus: depth map

A depth map for the published plate p-shop-plus.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 72.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

P stick cafe: depth map

A depth map for the published plate p-stick-cafe.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 82.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

P tailor mid: depth map

A depth map for the published plate p-tailor-mid.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P teacher cls: depth map

A depth map for the published plate p-teacher-cls.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

P trades w: depth map

A depth map for the published plate p-trades-w.png, 1024x1497. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 72.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1497 px
Use case: Depth estimation

P ware young: depth map

A depth map for the published plate p-ware-young.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Plate food: depth map

A depth map for the published plate plate-food.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr backpack: depth map

A depth map for the published plate pr-backpack.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 95.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr bottle: depth map

A depth map for the published plate pr-bottle.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 44.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr headphone: depth map

A depth map for the published plate pr-headphone.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr laptop: depth map

A depth map for the published plate pr-laptop.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 87.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr plant pot: depth map

A depth map for the published plate pr-plant-pot.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 69.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr shoe: depth map

A depth map for the published plate pr-shoe.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 52.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Pr watch: depth map

A depth map for the published plate pr-watch.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 92.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Receipt counter: depth map

A depth map for the published plate receipt-counter.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

S bakery: depth map

A depth map for the published plate s-bakery.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

S cafe empty: depth map

A depth map for the published plate s-cafe-empty.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 88.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S kitchen clean: depth map

A depth map for the published plate s-kitchen-clean.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 85.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S market stall: depth map

A depth map for the published plate s-market-stall.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 96.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S office: depth map

A depth map for the published plate s-office.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 92.8% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

S office open: depth map

A depth map for the published plate s-office-open.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 94.6% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S plate: depth map

A depth map for the published plate s-plate.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 92.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S shop front: depth map

A depth map for the published plate s-shop-front.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S warehouse a: depth map

A depth map for the published plate s-warehouse-a.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.1% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

S workshop: depth map

A depth map for the published plate s-workshop.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 91.4% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

Sp climbing: depth map

A depth map for the published plate sp-climbing.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 81.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Sp cyclists: depth map

A depth map for the published plate sp-cyclists.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 93.3% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Sp football: depth map

A depth map for the published plate sp-football.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 85.2% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Sp gym: depth map

A depth map for the published plate sp-gym.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 88.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Sp swimmer: depth map

A depth map for the published plate sp-swimmer.png, 1024x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 77.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1024 × 1024 px
Use case: Depth estimation

Storefront: depth map

A depth map for the published plate storefront.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 97.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation

T bokeh warm: depth map

A depth map for the published plate t-bokeh-warm.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T grad cool: depth map

A depth map for the published plate t-grad-cool.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T grad mono: depth map

A depth map for the published plate t-grad-mono.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 100.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T grad warm: depth map

A depth map for the published plate t-grad-warm.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.7% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T mesh grad: depth map

A depth map for the published plate t-mesh-grad.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 88.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T tex concrete: depth map

A depth map for the published plate t-tex-concrete.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 100.0% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T tex linen: depth map

A depth map for the published plate t-tex-linen.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 98.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

T tex paper: depth map

A depth map for the published plate t-tex-paper.png, 1536x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.9% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1536 × 1024 px
Use case: Depth estimation

Warehouse clip: depth map

A depth map for the published plate warehouse-clip.png, 1497x1024. Monocular depth from Depth Anything V2 (vitl), rendered at the plate's long edge rather than the 512-pixel default, so depth and colour can be compared per pixel without a resample in between. Lit coverage measures 99.5% of the frame. The plate and this map are the same scene at the same size, so they can be compared pixel for pixel rather than by eye.

File: PNG · Ai Vision · 1497 × 1024 px
Use case: Depth estimation