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igev-plusplus

Depth Estimation
PyTorch
fastforks
stereo
stereo-matching
disparity
cuda
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metadata 
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: 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 1278 1081 393 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 1081 715.6 159.0 494.4 425 393 1594 / 1840 952 / 1074 3632

Fixed-size speed and peak memory, all models

model mode 480x640 720x1280 1080x1920 12 MP 3000x4000
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 (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

On public datasets

Original = the authors' code, same checkpoint and protocol.

variant dataset original fp32 original fp16 portable kernels note
igev-plusplus-sceneflow ETH3D two-view train, bad > 1 px % / EPE 4.66 / 0.356 4.66 / 0.355 4.65 / 0.355 4.66 / 0.356
igev-plusplus-sceneflow Middlebury 2014 trainingH, bad > 2 px % / EPE 10.57 / 3.120 10.58 / 3.120 10.55 / 3.128 10.56 / 3.153
igev-plusplus-kitti12 ETH3D two-view train, bad > 1 px % / EPE 26.75 / 1.968 26.77 / 1.966 26.73 / 1.969 26.78 / 1.963
igev-plusplus-kitti12 Middlebury 2014 trainingH, bad > 2 px % / EPE 21.25 / 4.995 21.25 / 4.990 21.25 / 4.996 21.25 / 4.976
igev-plusplus-kitti15 ETH3D two-view train, bad > 1 px % / EPE 25.52 / 1.896 25.51 / 1.893 25.47 / 1.888 25.50 / 1.890
igev-plusplus-kitti15 Middlebury 2014 trainingH, bad > 2 px % / EPE 20.69 / 4.856 20.70 / 4.861 20.71 / 4.851 20.69 / 4.859
igev-plusplus-middlebury ETH3D two-view train, bad > 1 px % / EPE 18.40 / 0.779 18.31 / 0.778 18.47 / 0.780 18.46 / 0.782 possibly in the training data
igev-plusplus-middlebury Middlebury 2014 trainingH, bad > 2 px % / EPE 4.70 / 0.703 4.66 / 0.703 4.76 / 0.707 4.79 / 0.708 trained on this dataset's public images; not a zero-shot result
igev-plusplus-eth3d ETH3D two-view train, bad > 1 px % / EPE 0.50 / 0.093 0.50 / 0.094 0.50 / 0.093 0.50 / 0.094 trained on this dataset's public images; not a zero-shot result
igev-plusplus-eth3d Middlebury 2014 trainingH, bad > 2 px % / EPE 10.61 / 2.709 10.62 / 2.649 10.58 / 2.713 10.60 / 2.734

Measured on portable: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-05; kernels: NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, 2026-11-06.

Parity with the original code

Every variant is checked against reference 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")  # 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: 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.