C-FoundationStereo

• Original code: https://github.com/NVIDIA/tao_pytorch_backend at 77fb1a4bca02
• Code licence: Apache-2.0 (permissive); files: LICENSE, NOTICE
• Weights licence: LicenseRef-NVIDIA-Open-Model-License
  • c-fast-foundationstereo: LicenseRef-NVIDIA-Open-Model-Agreement
  • fast-foundationstereo: LicenseRef-NVIDIA-Source-Code-License-NC
• Licence evidence: https://huggingface.co/nvidia/c-foundationstereo-s/blob/d776ba855977e99cc9f16246f911d701da0bbc7a/README.md
• Training data: Foundation Stereo Dataset (FSD, NVIDIA synthetic), CREStereo synthetic stereo dataset (Megvii), TartanAir (pretrained on the Foundation Stereo Dataset (FSD, NVIDIA synthetic) and CREStereo, fine-tuned on FSD and TartanAir)
  • c-fast-foundationstereo: Foundation Stereo Dataset (FSD, NVIDIA synthetic), NVIDIA internal proprietary data, Stereo4D (stereo YouTube videos) (large synthetic NVIDIA set (1.4 M pairs; 'internal, proprietary dataset') plus real Stereo4D)
  • fast-foundationstereo: Foundation Stereo Dataset (FSD, NVIDIA synthetic) (Foundation Stereo Dataset (FSD, NVIDIA synthetic) plus further real and synthetic data, distilled from FoundationStereo; no per-dataset terms checked)
• Licence note: fast-foundationstereo is the research checkpoint 23-36-37 (the original demo default, the most accurate released): weights are non-commercial (NVIDIA Source Code License NC); our code here is the Apache-2.0 TAO code, which does not change that. For commercial use take c-foundationstereo-s (its model card says 'ready for commercial use').
• Licence note: Trained on NVIDIA's FSD dataset, whose terms for commercial use of trained models are unclear; the status does not depend on the training data.
• Licence note: c-fast-foundationstereo: the weights are governed by the NVIDIA Open Model Agreement, which allows commercial use; the model card's description also calls the model 'for research and evaluation purposes only', which contradicts it. We follow the governing terms.
• Options: iters = 0 (>= 0)
• Kernel libraries: igev, cutlass_conv, cudnn_hacc, fastbb, fs, fs_cutlass, cfs, ffs_cutlass
• Variants: c-foundationstereo-s, c-fast-foundationstereo, fast-foundationstereo
• Outputs: disparity
• Input size: any H x W >= 32, padded to a multiple of 32
• Tested up to (portable, fp16, RTX 5060 Ti 16 GB): c-foundationstereo-s 1024x2048 (8.3 GiB peak allocated, 11.1 reserved); c-fast-foundationstereo 1988x2880 (9.3 GiB peak allocated, 12.3 reserved); fast-foundationstereo 1988x2880 (9.3 GiB peak allocated, 12.3 reserved)
• c-fast-foundationstereo 994x1482, first call: portable + torch.compile 75 s (cold Inductor cache), TensorRT fp16 240 s (ONNX + engine).
• c-foundationstereo-s original 994x1482: reference implementation (FoundationStereo repo code) with the C-FS-S checkpoint, AMP; NVIDIA's TAO code OOMs (fp32) or errors (AMP) in the same harness
• c-foundationstereo-s load / first call: original 1.67 s / 2.731 s (375x1242), portable 0.62 s / 3.647† s, kernels 0.4† s / 1.362† s
• c-fast-foundationstereo load / first call: original 0.17 s / 0.172 s (375x1242), portable 0.24 s / 0.332† s, kernels 0.16† s / 0.136† s
• fast-foundationstereo load / first call: original 0.16 s / 0.144 s (375x1242), portable 0.25 s / 0.33† s, kernels 0.14† s / 0.135† s
• † measured in the speed sweep after the portable load (or an earlier size) in the same process: file cache and kernels are warm, so the time is lower than a fresh start.
• Protocol, median of 5 after 3 warm-up, uint8 CUDA input (synthetic for bench_inputs sizes), card defaults; gc before each load; peak = max over the first call and the timed calls

Measured

Original = the original code named under Details, same checkpoint and protocol.

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 468fba10, 2026-11-05.

Parity

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.

Limitations

• ONNX export needs torch >= 2.14 (older exporters fail on this network).