BridgeDepth (fastforks)
BridgeDepth (Guan et al. 2025): Depth Anything V2 (ViT-L) features combined with NMRF stereo hypotheses; window-attention message passing between mono and stereo embeddings, inference at 1/8 resolution plus a 1/4 refinement.
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/aeolusguan/BridgeDepth at 9a231601e698.
Author weight repos: https://huggingface.co/aeolusguan/BridgeDepth.
Task: stereo. Tier: experimental. Outputs: disparity. Backends: portable, kernels.
Input: a rectified left / right image pair. Output: a disparity map (pixels, left view).
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 |
|---|---|---|---|---|
bridgedepth-rvc-pretrain@1 |
bridge_rvc_pretrain.pth | 4cb8d0395e67 |
CC-BY-NC-4.0 | no |
bridgedepth-sf@1 |
bridge_sf.pth | d2cd44b6f749 |
CC-BY-NC-4.0 | no |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: LICENSE, LICENSE-torchvision.
Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa).
- Licence note: The weights embed Depth Anything V2 weights released under CC-BY-NC-4.0, so they are non-commercial even though the code is MIT.
Licence evidence: https://github.com/aeolusguan/BridgeDepth/blob/9a231601e6981d3536fa6a78ebaccebc1edb99a4/README.md#L40
Input size
any H x W >= 16, padded to a multiple of 16
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): bridgedepth-rvc-pretrain 3000x4000 (13.3 GiB peak allocated, 14.2 reserved); bridgedepth-sf 3000x4000 (13.3 GiB peak allocated, 14.2 reserved)
Speed and memory
Stereo, 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 |
|---|---|---|---|---|---|---|---|---|---|
bridgedepth-rvc-pretrain |
628.7 | 470.1 | 441.62 | 353.63 | 1.3x | 1.2x | 2903 / 3200 | 2968 / 3246 | 2361 / 2686 |
bridgedepth-sf |
616.5 | 459.9 | 436.86 | 348.37 | 1.3x | 1.3x | 2852 / 3144 | 2916 / 3214 | 2310 / 2634 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
bridgedepth-rvc-pretrain |
5.87 (375x1242) | 1.84 | 0.76† | 1.36 (375x1242) | 0.45† | 0.36† |
bridgedepth-sf |
4.86 (375x1242) | 1.53 | 0.72† | 0.12 (375x1242) | 0.44† | 0.35† |
torch.compile and TensorRT
| model | input | portable ms | compile ms | compile s (cold) | TensorRT fp16 ms | ONNX + build s | kernels ms | peak MiB (alloc / reserved): portable | compile | TensorRT (device) |
|---|---|---|---|---|---|---|---|---|---|---|
bridgedepth-sf |
994x1482 | 436.86 | 325.1 | 111.4 | fails: TensorRT engine build failed | – | 348.37 | 2916 / 3214 | 2280 / 2308 | – |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
bridgedepth-rvc-pretrain |
portable | 76.7 ms / 1727 / 1798 | 247.2 ms / 2359 / 2628 | 676.5 ms / 3562 / 4102 | 7918.7 ms (n=3) / 13620 / 14590 |
bridgedepth-rvc-pretrain |
kernels | 62.2 ms / 1147 / 1266 | 200.9 ms / 1764 / 2096 | 534.7 ms / 2939 / 3514 | 5573.1 ms (n=3) / 12950 / 13592 |
bridgedepth-sf |
portable | 73.2 ms / 1676 / 1748 | 242.8 ms / 2308 / 2560 | 668.4 ms / 3511 / 4032 | 7849.6 ms (n=3) / 13569 / 14540 |
bridgedepth-sf |
kernels | 59.1 ms / 1096 / 1214 | 196.3 ms / 1714 / 2044 | 526.9 ms / 2888 / 3462 | 5498.6 ms (n=3) / 12898 / 13540 |
Apple silicon (MPS)
| model | portable |
kernels |
speed-up |
|---|---|---|---|
bridgedepth-sf |
1065 ms / 4145 MiB (float32) | 1012 ms / 4093 MiB | 1.05x |
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
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| <= 0.02 px and p99 <= 0.25 px.
Usage
from fastforks import stereo
# non-commercial weights: run with FASTFORKS_NONCOMMERCIAL=1 in the environment
model = stereo.load("bridgedepth-rvc-pretrain") # mode="auto": the preferred backend from recorded measurements
result = model("left.png", "right.png")
result.disparity # [H, W] float32 tensor, input resolution
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: bridgedepth-rvc-pretrain, bridgedepth-sf.
Kernels backend
stereo.load("bridgedepth-rvc-pretrain", 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.