- Monocular Depth Estimation
- Video Depth Estimation
- Relative Camera Pose Estimation
- Multi-view Reconstruction (Point Map Estimation)
The root config file of all evaluations is configs/eval.yaml, however you don't need to edit it
- All main hyperparameters you need are in
configs/evaluation/xxxxx.yaml - Sometimes you may want to change the dataset config in
configs/data/xxxxx.yaml, or the model config inconfigs/model/xxxxx.yaml
Please put all evaluation datasets under data folder, or you can change the config in configs/data/xxxxx.yaml.
For data preprocessing:
- Depth Estimation: We follow MonST3R to prepare Sintel, Bonn, KITTI and NYU-v2.
- Camera Pose Estimation
- Angular: We follow VGGT to prepare Co3Dv2, and we afford our script for RealEstate10k preprocessing.
- Distance: We follow MonST3R to prepare Sintel, TUM-dynamics and ScanNetv2.
- Point Map Estimation: We follow Spann3R to prepare 7-Scenes, Neural-NRGBD and DTU. We afford our script for ETH3D preprocessing.
We provide reference-only preprocessing scripts under
datasets/preprocess. Please ensure you have obtained the necessary licenses from the original dataset providers before proceeding.
See monodepth/README.md for more details.
python monodepth/infer.py
python monodepth/eval.pyconfigs in configs/evaluation/videodepth.yaml, see videodepth/README.md for more details.
python videodepth/infer.py
python videodepth/eval.pyconfigs in configs/evaluation/relpose-angular.yaml, see relpose/README.md for more details.
# python relpose/sampling.py # to generate seq-id-maps under datasets/seq-id-maps, which is provided in this repo
python relpose/eval_angle.pypython relpose/eval_dist.pySee mv_recon/README.md for more details.
# python mv_recon/sampling.py # to generate seq-id-maps under datasets/seq-id-maps, which is provided in this repo
python mv_recon/eval.pyOur work builds upon several fantastic open-source projects. We'd like to express our gratitude to the authors of:
If you find our work useful, please consider citing:
@misc{wang2025pi3,
title={$\pi^3$: Scalable Permutation-Equivariant Visual Geometry Learning},
author={Yifan Wang and Jianjun Zhou and Haoyi Zhu and Wenzheng Chang and Yang Zhou and Zizun Li and Junyi Chen and Jiangmiao Pang and Chunhua Shen and Tong He},
year={2025},
eprint={2507.13347},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.13347},
}For academic use, this project is licensed under the 2-clause BSD License. See the LICENSE file for details. For commercial use, please contact the authors.