MonSter++

• Original code: https://github.com/Junda24/MonSter-plusplus at 9167101af8fb
• Code licence: LicenseRef-noncommercial (noncommercial); files: LICENSE
• Weights licence: LicenseRef-noncommercial AND CC-BY-NC-4.0
• Licence evidence: https://github.com/Junda24/MonSter-plusplus/blob/9167101af8fbe93c706707daa7a8181089c8b1b4/LICENSE#L34
• Training data: mix_all: 'all the datasets we collect over 2M image pairs' (README), not listed
• Options: iters = 32 (>= 1)
• Kernel libraries: igev, igevpp, cutlass_conv, cudnn_hacc, fastbb
• Variants: monster-plusplus-mix-all
• 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): monster-plusplus-mix-all 1988x2880 (7.7 GiB peak allocated, 9.6 reserved)
• monster-plusplus-mix-all 994x1482, first call: portable + torch.compile 109 s (cold Inductor cache), TensorRT fp16 416 s (ONNX + engine).
• monster-plusplus-mix-all load / first call: original 8.37 s / 1.233 s (375x1242), portable 2.45 s / 1.633† s, kernels 0.79† s / 0.698† 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| <= 0.2 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.03 px and p99 <= 0.25 px.