fastforks/igev-plusplus

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: https://github.com/kleinicke/fastforks. Kernels for the kernels backend: https://huggingface.co/fastforks/kernels. Original code: https://github.com/gangweix/IGEV-plusplus 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.

variant	weights (author link)	SHA-256	weights licence	commercial use
igev-plusplus-eth3d@1	eth3d.pth	4595ac1c164b	MIT (assumed from code)	yes
igev-plusplus-kitti12@1	kitti2012.pth	2e7947101eec	MIT (assumed from code)	yes
igev-plusplus-kitti15@1	kitti2015.pth	42db237df5f8	MIT (assumed from code)	yes
igev-plusplus-middlebury@1	middlebury.pth	eecddfa4549d	MIT (assumed from code)	yes
igev-plusplus-sceneflow@1	sceneflow.pth	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: https://github.com/gangweix/IGEV-plusplus/blob/099dec97989780d151700459c682df1cc2c18e88/README.md#L23

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†

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

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 (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

from fastforks import stereo

model = stereo.load("igev-plusplus-eth3d")  # portable PyTorch on the GPU if there is one, else the CPU
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: 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 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.