Models · Multi-view depth

VGGT-Omega

VGGT-Omega (Meta): the successor of VGGT with a DINOv3 backbone, cameras and depth from many images. Non-commercial licence.

Commercial use: no (code: LicenseRef-FAIR-Noncommercial-Research (noncommercial); weights: LicenseRef-FAIR-Noncommercial-Research).

variantinputbackendtime (ms)peak memory (MiB, allocated / reserved)
vggt-omega-1b-512synthetic (336x518)original (fp32)
original
portable
kernels
portable + torch.compile
TensorRT fp16
2650
906
726
464
589
377
5765 / 5912
7495 / 7654
3421 / 3518
3422 / 3518
3082 / 3154
2994 (engine)
Details
  • Original code: https://github.com/facebookresearch/vggt-omega at 48b23c8
  • Code licence: LicenseRef-FAIR-Noncommercial-Research (noncommercial); files: LICENSE
  • Weights licence: LicenseRef-FAIR-Noncommercial-Research
  • Licence evidence: https://github.com/facebookresearch/vggt-omega/blob/48b23c8/LICENSE and https://huggingface.co/facebook/VGGT-Omega/blob/1041e80fc0e911235d3426b0a3d9a81075111a53/LICENSE.txt
  • Training data: Per the repository README: a mixture of public multi-view datasets (uCO3D annotations released); DINOv3 initialisation. The 512 checkpoint is recommended for in-the-wild use, not benchmarking (vggt_omega_1b_416_reproduce.pt is the benchmark reference).
  • Licence note: Code and weights are under the FAIR Noncommercial Research License; the checkpoint is gated on Hugging Face (accept the licence there) and not mirrored.
  • Options: area_side = 512 (>= 16; also resolution)
  • Kernel libraries: fastbb, vggt_omega
  • Variants: vggt-omega-1b-512
  • Outputs: depth, confidence, intrinsics, cam_to_world; kind: scale_invariant
  • Input size: any H x W >= 1, resized or padded inside the model
  • Tested up to (portable, fp16, RTX 5060 Ti 16 GB): vggt-omega-1b-512 3000x4000 (5.5 GiB peak allocated, 6.7 reserved)
  • vggt-omega-1b-512 336x518, first call: portable + torch.compile 66 s (cold Inductor cache), TensorRT fp16 187 s (ONNX + engine).
  • vggt-omega-1b-512 original (fp32) 336x518: float32 (the authors' autocast off)
  • vggt-omega-1b-512 original 336x518: original default VGGTOmega(autocast=True): bfloat16 aggregator, float32 heads
  • vggt-omega-1b-512 load / first call: original 16.31 s / 1.1 s (336x518), portable 3.31 s / 0.734 s (336x518), kernels 2.27† s / 0.461 s (336x518)
  • † 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 9 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

640x512 crops, the 7 most co-visible views as sources; GT = depth rendered from the structured-light points. AbsRel / delta1 over GT pixels, after per-image median scaling unless the model takes the cameras (calibrated MVS: cameras, error in mm; PatchMatchNet also the depth range 425-935 mm, MVSAnywhere its own range from the camera baselines). Original = the original code named under Details, same checkpoint and protocol.

variantdatasetoriginal fp32original fp16portablekernelsnote
vggt-omega-1b-512DTU scans 1, 9, 24, 33, 75, view 24 + 7 sources, AbsRel / delta1 %0.0050 / 99.930.0051 / 99.930.0050 / 99.930.0051 / 99.93

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

Parity

CPU, float32: max |diff| <= 0.002 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 0.02 and p99 <= 0.25 (absolute error of the output depth).

variantkernels error vs original, mean / p99GPU, torchkernels used
vggt-omega-1b-5120.0002 / 0.00065RTX 5060 Ti, torch 2.14.1+cu130fastbb@7d058322634d:sm_120, vggt_omega@b8fa01afd00c:sm_120