MVSAnywhere (fastforks)
MVSAnywhere (Izquierdo et al. 2025): zero-shot multi-view stereo, the depth of a reference view from posed, calibrated source views. A view-agnostic MLP cost volume (warped matching features + camera metadata, softmax over the views) is encoded by a DINOv2 ViT-B fused with a second DINOv2's image features and decoded by a DPT head; the depth range comes from the camera baselines and is refined once.
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/nianticlabs/mvsanywhere at 5bd49bb.
Task: multiview. Tier: experimental. Outputs: depth, mask, sky, depth_range. Backends: portable, kernels.
Input: several images of one scene (posed and calibrated for multi-view stereo, unposed otherwise). Output: depth maps and, where the model predicts them, cameras and 3-D points.
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 |
|---|---|---|---|---|
mvsanywhere@1 |
mvsanywhere_hero.ckpt | 00878bab903b |
LicenseRef-noncommercial AND CC-BY-NC-SA-4.0 (assumed from code) | no |
Licence
Original-code strings, recorded as they are (research-use package; check them before any other use). Code: LicenseRef-noncommercial AND CC-BY-NC-SA-4.0. Files: LICENSE, LICENSE-antialiased-cnns.md.
Code licence: LicenseRef-noncommercial AND CC-BY-NC-SA-4.0; the authors do not state a separate weights licence.
Training data: Synthetic data per the paper (arXiv 2411.14390): Hypersim, TartanAir, BlendedMVG, MatrixCity, VKITTI2, Dynamic Replica, MVS-Synth, SAIL-VOS 3D; DINOv2 / Depth Anything V2 initialisation.
- Licence note: Niantic's non-commercial licence covers the code and the checkpoint; the BlurPool stem comes from Adobe's antialiased-cnns (CC-BY-NC-SA-4.0).
Licence evidence: https://github.com/nianticlabs/mvsanywhere/blob/5bd49bb/LICENSE and README.md (the checkpoint is published under the repository's licence)
Input size
any H x W >= 1, resized or padded inside the model
Tested up to (portable, fp16, RTX 5060 Ti 16 GB): mvsanywhere 3000x4000 (3.0 GiB peak allocated, 4.2 reserved)
Speed and memory
Multiview, synthetic, 448x672
| model | time (ms): original fp32 | portable | kernels | kernels speed-up: vs portable | peak memory (MiB, allocated / reserved): portable | kernels |
|---|---|---|---|---|---|---|
mvsanywhere |
273.23 | 93.22 | 35.63 | 2.6x | 931 / 1226 | 761 / 884 |
Startup and first call
| model | load (s): original | portable | kernels | first call (s): original | portable | kernels |
|---|---|---|---|---|---|---|
mvsanywhere |
3.25 (448x672) | 1.88 (448x672) | 0.52† (448x672) | 0.35 (448x672) | 0.22 (448x672) | 0.05 (448x672) |
Fixed-size speed and peak memory, all models
| model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 |
|---|---|---|---|---|---|
mvsanywhere |
portable | 107.5 ms / 969 / 1500 | 75.8 ms / 954 / 1398 | 77.4 ms / 1166 / 1756 | 124.0 ms / 3110 / 4342 |
mvsanywhere |
kernels | 40.6 ms / 769 / 1006 | 31.0 ms / 838 / 1068 | 32.5 ms / 1049 / 1414 | 57.1 ms / 2908 / 4006 |
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| <= 0.1 % and p99 <= 0.5 % of the reference's median |value|.
Usage
from fastforks import multiview
model = multiview.load("mvsanywhere") # portable PyTorch on the GPU if there is one, else the CPU
result = model(["view0.jpg", "view1.jpg", "view2.jpg"]) # the views of one scene
result.depth # [V, H, W] float32 tensor; result.kind says what the maps mean
# calibrated MVS: model(views, intrinsics=K, cam_to_world=c2w), K [N, 3, 3], c2w [N, 4, 4]
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: mvsanywhere.
Kernels backend
multiview.load("mvsanywhere", mode="kernels") runs our CUDA kernels (fastbb, igev, cutlass_conv, cudnn_hacc, mvsanywhere). 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.