Andreas Geiger

Publications of Haofei Xu

MuRF: Multi-Baseline Radiance Fields
H. Xu, A. Chen, Y. Chen, C. Sakaridis, Y. Zhang, M. Pollefeys, A. Geiger and F. Yu
Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Abstract: We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views). To render a target novel view, we discretize the 3D space into planes parallel to the target image plane, and accordingly construct a target view frustum volume. Such a target volume representation is spatially aligned with the target view, which effectively aggregates relevant information from the input views for high-quality rendering. It also facilitates subsequent radiance field regression with a convolutional network thanks to its axis-aligned nature. The 3D context modeled by the convolutional network enables our method to synthesis sharper scene structures than prior works. Our MuRF achieves state-of-the-art performance across multiple different baseline settings and diverse scenarios ranging from simple objects (DTU) to complex indoor and outdoor scenes (RealEstate10K and LLFF). We also show promising zero-shot generalization abilities on the Mip-NeRF 360 dataset, demonstrating the general applicability of MuRF.
Latex Bibtex Citation:
  author = {Haofei Xu and Anpei Chen and Yuedong Chen and Christos Sakaridis and Yulun Zhang and Marc Pollefeys and Andreas Geiger and Fisher Yu},
  title = {MuRF: Multi-Baseline Radiance Fields},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2024}
Unifying Flow, Stereo and Depth Estimation
H. Xu, J. Zhang, J. Cai, H. Rezatofighi, F. Yu, D. Tao and A. Geiger
Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023
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 = {Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year = {2023}

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