Metric3D v2 (fastforks)
Metric3D v2 (Hu et al. 2024): metric depth and surface normals from one image; a DINOv2-reg ViT-L encoder and a RAFT-style iterative depth / normal decoder in a canonical camera space (focal 1000 px), rescaled by the given focal length.
This repo is the model card only: it holds no weights. fastforks downloads the authors' weights from the links below and checks them against the SHA-256 in the model card. Code: https://github.com/kleinicke/fastforks. Kernels for the kernels backend: https://huggingface.co/fastforks/kernels. Original code: https://github.com/YvanYin/Metric3D at eb5b6fa.
Author weight repos: https://huggingface.co/JUGGHM/Metric3D.
Task: depth. Tier: experimental. Outputs: depth, normals, confidence. Backends: portable, kernels.
Input: one RGB image. Output: a depth or inverse-depth map (metric for the models marked metric).
Variants
Weights are the authors' files (not hosted here). SHA-256 shortened to 12 hex digits; the full hashes are in card.toml.
| variant | weights (author link) | SHA-256 | weights licence | commercial use |
|---|---|---|---|---|
metric3d-vit-large@1 |
metric_depth_vit_large_800k.pth | 15328ffc42b5 |
BSD-2-Clause (assumed from code) | yes |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: BSD-2-Clause. Files: LICENSE.
Code licence: BSD-2-Clause; the authors do not state a separate weights licence.
Training data: 16M images from 18 public metric-depth datasets (indoor, outdoor, driving) per the Metric3D v2 paper (arXiv 2404.15506); original asks commercial users to contact the authors.
- Licence note: The upstream README says 'for further commercial inquiries, please contact' the authors; no weights licence is stated, so the BSD-2-Clause code licence is assumed.
Licence evidence: https://github.com/YvanYin/Metric3D/blob/eb5b6fac0dc155e4e52f576e304fbf11655ff339/README.md#license-and-contact
Input size
any H x W >= 8, resized or padded inside the model
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): metric3d-vit-large 3000x4000 (2.3 GiB peak allocated, 2.9 reserved)
Speed and memory
Depth, Middlebury 2014 half, 994x1482
| model | time (ms): original fp32 | original | portable | kernels | kernels speed-up: vs original | vs portable | peak memory (MiB, allocated / reserved): original | portable | kernels |
|---|---|---|---|---|---|---|---|---|---|
metric3d-vit-large |
628.7 | 438.5 | 200.31 | 158.46 | 2.8x | 1.3x | 3275 / 3350 | 1484 / 1696 | 1516 / 1688 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
metric3d-vit-large |
9.96 | 3.45 | 0.31† | 0.88 | 0.23 | 0.16 |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
metric3d-vit-large |
portable | 207.0 ms / 2219 / 2422 | 207.0 ms / 2228 / 2440 | 207.3 ms / 2242 / 2428 | 211.9 ms / 2401 / 2926 |
metric3d-vit-large |
kernels | 158.4 ms / 1471 / 1796 | 158.4 ms / 1478 / 1812 | 158.8 ms / 1492 / 1822 | 162.9 ms / 1616 / 2022 |
Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB).
Hardware, protocol and caveats: benchmarks (RTX 5060 Ti 16 GB, sm_120). Original = the authors' code at the precision of portable (fp16, or (fp32) where fp16 is unsupported or not finite); original fp32 = its default fp32 run. Peak memory is the torch allocator's.
Accuracy
On public datasets
AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the authors' code, same checkpoint and protocol.
| variant | dataset | original | portable | kernels | note |
|---|---|---|---|---|---|
metric3d-vit-large |
NYUv2 test, AbsRel / delta1 | 0.0546 / 0.9676 | 0.0547 / 0.9673 | 0.0546 / 0.9673 | |
metric3d-vit-large |
iBims-1, AbsRel / delta1 | 0.0334 / 0.9853 | 0.0334 / 0.9855 | 0.0334 / 0.9855 | |
metric3d-vit-large |
ETH3D two-view train left, AbsRel / delta1 | 0.0746 / 0.9406 | 0.0740 / 0.9410 | 0.0741 / 0.9410 | possibly in the training data |
metric3d-vit-large |
Middlebury 2014 trainingH left, AbsRel / delta1 | 0.0248 / 0.9961 | 0.0247 / 0.9961 | 0.0247 / 0.9961 | possibly in the training data |
Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-10-27.
Parity with the original code
Every variant is checked against golden outputs of the original code at its pinned commit. CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1 % and p99 <= 3 % of the reference's median |value|.
Limitations
- The normals convention (camera axes / sign) is not checked against a dataset; it is passed through as original returns it.
Usage
from fastforks import depth
model = depth.load("metric3d-vit-large") # portable PyTorch on the GPU if there is one, else the CPU
result = model("room.jpg")
result.depth # [H, W] float32 tensor; result.kind says what the maps mean
# models that take a camera: model("room.jpg", intrinsics=K), K [3, 3] in pixels
result.backend # what ran, e.g. Backend(mode='portable', device='cuda:0', dtype='float16')
Install with pip install fastforks (see https://github.com/kleinicke/fastforks). Other variants: metric3d-vit-large.
Kernels backend
depth.load("metric3d-vit-large", mode="kernels") runs our CUDA kernels (fastbb). It needs a prebuilt kernel for your GPU architecture (downloaded from https://huggingface.co/fastforks/kernels on first use) and a recent NVIDIA driver; if there is none it raises BackendUnavailableError with the reason and never falls back silently. mode="auto" picks kernels when it can run and logs the choice.