Models · Monocular depth

MoGe

Microsoft's MoGe: depth, 3D points, normals and focal length from one image. Commercially usable.

Commercial use: yes.

variantinputbackendtime (ms)peak memory (MiB, allocated / reserved)
moge-2-vits-normalMiddlebury 2014 half (994x1482)original (fp32)
original
portable
kernels
portable + torch.compile
TensorRT fp16
162
82.6
64.2
29.7
43.9
24.5
1491 / 1628
1193 / 1268
1066 / 1336
884 / 1008
836 / 992
620 (engine)
moge-2-vitl-normalMiddlebury 2014 half (994x1482)original (fp32)
original
portable
kernels
501
171
144
73.4
2646 / 2818
2339 / 2438
1661 / 1836
1498 / 1610
moge-3-vitlMiddlebury 2014 half (994x1482)original (fp32)
original
portable
kernels
843
486
514
215
2812 / 2990
2505 / 2630
1686 / 1924
1673 / 1884
Details
  • Original code: https://github.com/microsoft/MoGe at 74fbce0
  • Code licence: MIT (permissive); files: LICENSE
  • Weights licence: MIT
  • Licence evidence: https://github.com/microsoft/MoGe/blob/main/README.md (License section) and the Hugging Face model cards (license: mit)
  • Training data: Mixture of public synthetic and real RGB-D / multi-view datasets per the MoGe-2 and MoGe-3 papers
  • Options: resolution_level = 9 (0 to 9), normals = False
  • Kernel libraries: fastbb, dpt_fused, sparse_conv
  • Variants: moge-2-vits-normal, moge-2-vitl-normal, moge-3-vitl
  • Outputs: depth, points, intrinsics, mask, normals; 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): moge-2-vits-normal 3000x4000 (1.2 GiB peak allocated, 1.4 reserved); moge-2-vitl-normal 3000x4000 (2.4 GiB peak allocated, 2.5 reserved); moge-3-vitl 3000x4000 (2.6 GiB peak allocated, 3.0 reserved)
  • moge-2-vits-normal 994x1482, first call: portable + torch.compile 124 s (cold Inductor cache), TensorRT fp16 131 s (ONNX + engine).
  • moge-2-vits-normal load / first call: original 0.88 s / 1.501 s, portable 0.35 s / 0.084 s, kernels 0.11† s / 0.099 s
  • moge-2-vitl-normal load / first call: original 7.24 s / 0.171 s, portable 1.37 s / 0.144 s, kernels 0.63† s / 0.131 s
  • moge-3-vitl load / first call: original 10.6 s / 2.918 s, portable 1.02 s / 0.562 s, kernels 1.66 s / 0.301 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, moge-2-vits-normal, moge-2-vitl-normal: 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
  • Protocol, moge-3-vitl: first call, 3 warm-up, peak reset, median of 3 rounds x median of 5

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.

variantdatasetoriginal fp32original fp16portablekernelsnote
moge-2-vits-normalNYUv2 test, AbsRel / delta10.0484 / 0.97570.0484 / 0.97570.0484 / 0.97570.0484 / 0.9757
moge-2-vits-normaliBims-1, AbsRel / delta10.0430 / 0.97930.0430 / 0.97930.0430 / 0.97930.0430 / 0.9793
moge-2-vits-normalETH3D two-view train left, AbsRel / delta10.0700 / 0.94120.0700 / 0.94110.0700 / 0.94110.0701 / 0.9412possibly in the training data
moge-2-vits-normalMiddlebury 2014 trainingH left, AbsRel / delta10.0356 / 0.99470.0356 / 0.99470.0356 / 0.99470.0356 / 0.9947possibly in the training data
moge-2-vitl-normalNYUv2 test, AbsRel / delta10.0357 / 0.98360.0357 / 0.98360.0357 / 0.98360.0357 / 0.9836
moge-2-vitl-normaliBims-1, AbsRel / delta10.0261 / 0.98920.0261 / 0.98920.0261 / 0.98920.0261 / 0.9892
moge-2-vitl-normalETH3D two-view train left, AbsRel / delta10.0518 / 0.97890.0518 / 0.97890.0519 / 0.97890.0518 / 0.9790possibly in the training data
moge-2-vitl-normalMiddlebury 2014 trainingH left, AbsRel / delta10.0235 / 0.99720.0235 / 0.99720.0235 / 0.99720.0235 / 0.9972possibly in the training data
moge-3-vitlNYUv2 test, AbsRel / delta10.0356 / 0.98360.0356 / 0.9836
moge-3-vitliBims-1, AbsRel / delta10.0261 / 0.98840.0261 / 0.9883
moge-3-vitlETH3D two-view train left, AbsRel / delta10.0496 / 0.98470.0496 / 0.9847possibly in the training data
moge-3-vitlMiddlebury 2014 trainingH left, AbsRel / delta10.0235 / 0.99700.0235 / 0.9970possibly in the training data

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

Parity

CPU, float32: max |diff| <= 0.005 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1.5 % and p99 <= 1.5 % of the reference's median |value| (absolute error of the output depth).

variantkernels error vs original, mean / p99GPU, torchkernels used
moge-2-vits-normal0.00116 / 0.0013RTX 5060 Ti, torch 2.14.1+cu130dpt_fused@48e0065bb7cf:sm_120, fastbb@7d058322634d:sm_120, sparse_conv@2feb2d5e0877:sm_120
moge-2-vitl-normal0.00382 / 0.00394RTX 5060 Ti, torch 2.14.1+cu130dpt_fused@48e0065bb7cf:sm_120, fastbb@7d058322634d:sm_120, sparse_conv@2feb2d5e0877:sm_120
moge-3-vitl0.00421 / 0.0049RTX 5060 Ti, torch 2.14.1+cu130dpt_fused@48e0065bb7cf:sm_120, fastbb@7d058322634d:sm_120, sparse_conv@2feb2d5e0877:sm_120