DLNR · fastforks

A stereo network in the RAFT-Stereo style with a decoupled LSTM and normalization refinement. Commercially usable.

Commercial use: yes.

variant	input	backend	time (ms)	peak memory (MiB, allocated / reserved)
`dlnr-sceneflow`	Middlebury 2014 half (994x1482)	original (fp32) original portable kernels portable + torch.compile TensorRT fp16	978 598 398 214 301 241	3191 / 3608 3004 / 3552 2635 / 3142 1236 / 1606 1343 / 1672 1492 (engine)

Details

Original code: https://github.com/David-Zhao-1997/High-frequency-Stereo-Matching-Network at c10475fe30ff
Code licence: MIT (permissive); files: LICENSE
Weights licence: MIT (assumed from code)
Licence evidence: https://github.com/David-Zhao-1997/High-frequency-Stereo-Matching-Network/blob/c10475fe30ff198781ecc7c712c24c781ff05096/README.md#L84
Training data: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa)
- dlnr-middlebury: Freiburg SceneFlow (FlyingThings3D, Driving, Monkaa), Middlebury stereo (2014) (SceneFlow, then fine-tuned on Middlebury (README; the training set is not stated))
Options: iters = 10 (>= 1)
Kernel libraries: raft, igev, cutlass_conv, cudnn_hacc, fastbb
Variants: dlnr-sceneflow, dlnr-middlebury
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): dlnr-sceneflow 1988x2880 (10.1 GiB peak allocated, 12.7 reserved); dlnr-middlebury 1988x2880 (10.1 GiB peak allocated, 12.7 reserved)
dlnr-middlebury: same network and settings as dlnr-sceneflow, measured once (rows not repeated).
dlnr-sceneflow 994x1482, first call: portable + torch.compile 69 s (cold Inductor cache), TensorRT fp16 161 s (ONNX + engine).
dlnr-sceneflow load / first call: original 0.83 s / 1.712 s (375x1242), portable 0.63 s / 0.399† s, kernels 0.26† s / 0.213† s
dlnr-middlebury load / first call: original 0.83 s / 1.712 s (375x1242), portable 0.63 s / 0.399† s, kernels 0.26† s / 0.213† 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.

variant	dataset	original fp32	original fp16	portable	kernels	note
`dlnr-sceneflow`	ETH3D two-view train, bad > 1 px % / EPE	30.26 / 5.931	30.27 / 5.924	30.26 / 5.935	30.27 / 5.944
`dlnr-sceneflow`	Middlebury 2014 trainingH, bad > 2 px % / EPE	11.42 / 1.650	11.43 / 1.650	11.42 / 1.651	11.42 / 1.650
`dlnr-middlebury`	ETH3D two-view train, bad > 1 px % / EPE	34.41 / 5.845	34.41 / 5.853	34.40 / 5.848	34.38 / 5.868
`dlnr-middlebury`	Middlebury 2014 trainingH, bad > 2 px % / EPE	7.17 / 0.989	7.18 / 0.988	7.18 / 0.988	7.18 / 0.992	trained on this dataset's public images; not a zero-shot result

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.

variant	kernels error vs original, mean / p99	GPU, torch	kernels used
`dlnr-sceneflow`	0.00216 / 0.03303	RTX 5060 Ti, torch 2.14.1+cu130	`cudnn_hacc@cudnn92400`, `cutlass_conv@988138e042cb:sm_120`, `fastbb@7d058322634d:sm_120`, `igev@f075827d68c6:sm_120`, `raft@9524a68234cc:sm_120`
`dlnr-middlebury`	0.00161 / 0.02686	RTX 5060 Ti, torch 2.14.1+cu130	`cudnn_hacc@cudnn92400`, `cutlass_conv@988138e042cb:sm_120`, `fastbb@7d058322634d:sm_120`, `igev@f075827d68c6:sm_120`, `raft@9524a68234cc:sm_120`