UniK3D

• Original code: https://github.com/lpiccinelli-eth/UniK3D at 29f7862
• Code licence: CC-BY-NC-SA-4.0 (noncommercial); files: LICENSE
• Weights licence: CC-BY-NC-SA-4.0 (assumed from code)
• Licence evidence: https://github.com/lpiccinelli-eth/UniK3D/blob/29f7862f3c3ee79c53ef0153dfc901ce511df575/LICENSE
• Training data: Mixture of public metric-depth datasets (pinhole, fisheye and panoramic cameras) per the UniK3D paper (arXiv 2503.16591)
• Options: resolution_level = 9 (0 to 9), confidence = True
• Kernel libraries: fastbb, dpt_fused
• Variants: unik3d-vitl
• Outputs: depth, points, confidence; kind: metric
• Input size: any H x W >= 8, resized or padded inside the model (ViT patch 14)
• Tested up to (portable, fp16, RTX 5060 Ti 16 GB): unik3d-vitl 3000x4000 (2.2 GiB peak allocated, 2.4 reserved)
• unik3d-vitl 994x1482, first call: portable + torch.compile 33 s (cold Inductor cache), TensorRT fp16 105 s (ONNX + engine).
• unik3d-vitl original 994x1482: torch.autocast(fp16) around the forward
• unik3d-vitl load / first call: original 7.28 s / 0.575 s, portable 4.16 s / 0.136 s, kernels 0.84† s / 0.067 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

AbsRel / delta1 after a least-squares scale + shift fit (in disparity space for inverse-depth models), mean over images. Original = the original code named under Details, same checkpoint and protocol.

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 939adfe8, 2026-11-06.

Parity

CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1 % and p99 <= 2 % of the reference's median |value| (absolute error of the output depth).