--- library_name: "fastforks" pipeline_tag: "depth-estimation" license: "other" license_name: "mit-code-weights-unstated" license_link: "https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23" tags: - "stereo" - "stereo-matching" - "disparity" - "cuda" - "pytorch" - "fastforks" --- # IGEV++ (fastforks) IGEV++ (Xu et al. 2025): IGEV-Stereo with geometry encoding volumes for small, medium and large disparity ranges (48 bins each at 1/4 resolution, up to 768 px), aggregated separately and blended per pixel in the ConvGRU update, 32 iterations. 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: . Kernels for the `kernels` backend: . Original code: at `099dec979897`. 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`](https://github.com/kleinicke/fastforks/blob/main/src/fastforks/stereo/models/igev_plusplus/card.toml). | variant | weights (author link) | SHA-256 | weights licence | commercial use | |---|---|---|---|---| | `igev-plusplus-eth3d@1` | [eth3d.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1gN8J9YVJMupJVjUAUMSJ0jldkqH1YJUo) | `4595ac1c164b` | MIT (assumed from code) | yes | | `igev-plusplus-kitti12@1` | [kitti2012.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1j5grw6AKhwhJNWJXn_RfhpFfiWBYboqH) | `2e7947101eec` | MIT (assumed from code) | yes | | `igev-plusplus-kitti15@1` | [kitti2015.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=15kWs23CO4bBDy3DJE4kNv_tlbeEvp1bk) | `42db237df5f8` | MIT (assumed from code) | yes | | `igev-plusplus-middlebury@1` | [middlebury.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1R9a2YP4kaAv4oT0wtwTnWXrb82Shpp3O) | `eecddfa4549d` | MIT (assumed from code) | yes | | `igev-plusplus-sceneflow@1` | [sceneflow.pth](https://drive.usercontent.google.com/download?export=download&confirm=t&id=1n8tu7eoByY_Xpyox5XJnjJC_bGBM2r4M) | `550227fb7b73` | MIT (assumed from code) | yes | ## Licence Original-code strings, recorded as they are (research-use package; check them before any other use). Code: MIT. Files: `LICENSE`. Code licence: MIT; the authors do not state a separate weights licence. Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa). Licence evidence: ## Input size any H x W >= 32, padded to a multiple of 32 Tested up to (portable, fp16, RTX 5060 Ti 16 GB): `igev-plusplus-eth3d` not measured yet; `igev-plusplus-kitti12` not measured yet; `igev-plusplus-kitti15` not measured yet; `igev-plusplus-middlebury` not measured yet; `igev-plusplus-sceneflow` not measured yet ## 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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `igev-plusplus-eth3d` | 2012.16 | 1277.86 | 1080.64 | 393.04 | 3.3x | 2.7x | 2891 / 3108 | 1594 / 1840 | 1389 / 2302 | ### Startup and first call | model | load (s): original | portable | kernels | first call (s): original | portable | kernels | | --- | --- | --- | --- | --- | --- | --- | | `igev-plusplus-eth3d` | 0.42 (375x1242) | 0.20 | 0.16† | 2.33 (375x1242) | 1.10† | 0.40† | ### 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) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | `igev-plusplus-eth3d` | 994x1482 | 1080.64 | 715.6 | 159.0 | 494.4 | 425 | 393.04 | 1594 / 1840 | 952 / 1074 | 3632 | ### Fixed-size speed and peak memory, all models | model | mode | 640x480 | 1280x720 | 1920x1080 | 12 MP 4000x3000 | | --- | --- | --- | --- | --- | --- | | `igev-plusplus-eth3d` | portable | 201.5 ms / 354 / 614 | 606.5 ms / 987 / 1662 | 1534.8 ms / 2172 / 3496 | OOM | | `igev-plusplus-eth3d` | kernels | 88.7 ms / 357 / 1092 | 241.2 ms / 830 / 1210 | 534.3 ms / 1799 / 2464 | 3135.8 ms (n=3) / 8419 / 9752 | | `igev-plusplus-kitti12` | portable | 201.4 ms / 354 / 614 | 607.2 ms / 987 / 1662 | 1535.8 ms / 2172 / 3496 | OOM | | `igev-plusplus-kitti12` | kernels | 89.1 ms / 357 / 1092 | 244.8 ms / 830 / 1210 | 535.9 ms / 1799 / 2464 | 3143.3 ms (n=3) / 8419 / 9750 | | `igev-plusplus-kitti15` | portable | 202.7 ms / 354 / 614 | 606.9 ms / 987 / 1662 | 1535.8 ms / 2172 / 3496 | OOM | | `igev-plusplus-kitti15` | kernels | 88.7 ms / 357 / 1092 | 240.2 ms / 830 / 1210 | 534.9 ms / 1799 / 2464 | 3141.8 ms (n=3) / 8419 / 9752 | | `igev-plusplus-middlebury` | portable | 201.2 ms / 354 / 612 | 606.8 ms / 987 / 1662 | 1535.6 ms / 2172 / 3496 | OOM | | `igev-plusplus-middlebury` | kernels | 88.7 ms / 357 / 1092 | 240.5 ms / 830 / 1210 | 535.0 ms / 1799 / 2464 | 3140.6 ms (n=3) / 8419 / 9752 | | `igev-plusplus-sceneflow` | portable | 201.0 ms / 354 / 612 | 606.2 ms / 987 / 1662 | 1534.9 ms / 2172 / 3496 | OOM | | `igev-plusplus-sceneflow` | kernels | 88.6 ms / 357 / 1092 | 240.6 ms / 830 / 1210 | 535.4 ms / 1799 / 2464 | 3143.9 ms (n=3) / 8419 / 9752 | ### Apple silicon (MPS) | model | `portable` | `kernels` | speed-up | | --- | --- | --- | --- | | `igev-plusplus-sceneflow` | 2852 ms / 1783 MiB (float16) | 2054 ms / 1803 MiB | 1.39x | Fixed-size cells: time (ms) / peak allocated / peak reserved memory (MiB). - `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`: same network and settings as `igev-plusplus-eth3d`, measured once (rows not repeated). Hardware, protocol and caveats: [benchmarks](https://github.com/kleinicke/fastforks/blob/main/docs/benchmarks.md) (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. ## Limitations - CUDA output (float16, kernels, float32 with PyTorch's default TF32 convolutions) differs from the CPU by ~0.06 px mean / 1.2 px p99 at 720x1280, 0.005 px with full float32: rounding amplified by 32 GRU iterations. Portable and kernels reach the same ETH3D / Middlebury accuracy. ## Usage ```python from fastforks import stereo model = stereo.load("igev-plusplus-eth3d") # 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 ). Other variants: `igev-plusplus-eth3d`, `igev-plusplus-kitti12`, `igev-plusplus-kitti15`, `igev-plusplus-middlebury`, `igev-plusplus-sceneflow`. ## Kernels backend `stereo.load("igev-plusplus-eth3d", mode="kernels")` runs our CUDA kernels (`igev`, `igevpp`, `cutlass_conv`, `cudnn_hacc`). It needs a prebuilt kernel for your GPU architecture (downloaded from 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.