Andreas Geiger

Publications of Haofei Xu

Unifying Flow, Stereo and Depth Estimation
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger
Arxiv, 2022
Abstract: We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images. Unlike previous specialized architectures for each specific task, we formulate all three tasks as a unified dense correspondence matching problem, which can be solved with a single model by directly comparing feature similarities. Such a formulation calls for discriminative feature representations, which we achieve using a Transformer, in particular the cross-attention mechanism. We demonstrate that cross-attention enables integration of knowledge from another image via cross-view interactions, which greatly improves the quality of the extracted features. Our unified model naturally enables cross-task transfer since the model architecture and parameters are shared across tasks. We outperform RAFT with our unified model on the challenging Sintel dataset, and our final model that uses a few additional task-specific refinement steps outperforms or compares favorably to recent state-of-the-art methods on 10 popular flow, stereo and depth datasets, while being simpler and more efficient in terms of model design and inference speed.
Latex Bibtex Citation:
  author = {Haofei Xu and Jing Zhang and Jianfei Cai and Hamid Rezatofighi and Fisher Yu and Dacheng Tao and Andreas Geiger},
  title = {Unifying Flow, Stereo and Depth Estimation},
  journal = {Arxiv},
  year = {2022}

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