OccAny: Generalized Unconstrained Urban 3D Occupancy

OccAny is a unified framework for generalized unconstrained urban 3D occupancy prediction. It is the first unconstrained urban 3D occupancy model capable of operating on out-of-domain uncalibrated scenes to predict and complete metric occupancy coupled with segmentation features from sequential, monocular, or surround-view images.

• Paper: OccAny: Generalized Unconstrained Urban 3D Occupancy
• Project Page: https://valeoai.github.io/OccAny
• Code: https://github.com/valeoai/OccAny

Model Variants

This repository hosts checkpoints for two model variants:

• OccAny: Based on Must3R and SAM2.
• OccAny+: Based on Depth Anything 3 and SAM3.

Sample Usage

After following the installation instructions in the GitHub repository, you can run inference using the following commands.

OccAny+ (Depth Anything 3 + SAM3)

python inference.py \
  --batch_gen_view 2 \
  --view_batch_size 2 \
  --semantic distill@SAM3 \
  --compute_segmentation_masks \
  --gen \
  -rot 30 \
  -vpi 2 \
  -fwd 5 \
  --seed_translation_distance 2 \
  --recon_conf_thres 2.0 \
  --gen_conf_thres 6.0 \
  --apply_majority_pooling \
  --model occany_da3

OccAny (Must3R + SAM2)

python inference.py \
  --batch_gen_view 2 \
  --view_batch_size 2 \
  --semantic distill@SAM2_large \
  --compute_segmentation_masks \
  --gen \
  -rot 30 \
  -vpi 2 \
  -fwd 5 \
  --seed_translation_distance 2 \
  --recon_conf_thres 2.0 \
  --gen_conf_thres 2.0 \
  --apply_majority_pooling \
  --model occany_must3r

Citation

If you find this work or code useful, please cite the paper:

@inproceedings{cao2026occany,
  title={OccAny: Generalized Unconstrained Urban 3D Occupancy},
  author={Anh-Quan Cao and Tuan-Hung Vu},
  booktitle={CVPR},
  year={2026}
}