Selected Publications
I have broad interests in computer vision, particularly in fundamental research problems like dense correspondences, motion, 3D and video representation learning. I like to explore simple and effective approaches to solving fundamental challenges. Please see the full publication list on Google Scholar .
DepthSplat: Connecting Gaussian Splatting and Depth
Haofei Xu ,
Songyou Peng ,
Fangjinhua Wang ,
Hermann Blum ,
Daniel Barath ,
Andreas Geiger ,
Marc Pollefeys
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DepthSplat enables cross-task interactions between Gaussian splatting and depth estimation.
MVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images
Yuedong Chen ,
Haofei Xu ,
Chuanxia Zheng ,
Bohan Zhuang ,
Marc Pollefeys ,
Andreas Geiger ,
Tat-Jen Cham ,
Jianfei Cai
European Conference on Computer Vision (ECCV ) , 2024 (Oral)
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A cost volume representation for efficiently predicting 3D Gaussians from sparse multi-view images in a single forward pass.
LaRa: Efficient Large-Baseline Radiance Fields
Anpei Chen ,
Haofei Xu ,
Stefano Esposito ,
Siyu Tang ,
Andreas Geiger
European Conference on Computer Vision (ECCV ) , 2024
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A feed-forward 2DGS model trained in two days using four GPUs.
Unifying Flow, Stereo and Depth Estimation
Haofei Xu ,
Jing Zhang ,
Jianfei Cai ,
Hamid Rezatofighi ,
Fisher Yu ,
Dacheng Tao ,
Andreas Geiger
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI ) , 2023
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A unified dense correspondence matching formulation enables three motion and 3D perception tasks to be solved with a unified model.
GMFlow: Learning Optical Flow via Global Matching
Haofei Xu ,
Jing Zhang ,
Jianfei Cai ,
Hamid Rezatofighi ,
Dacheng Tao
Computer Vision and Pattern Recognition (CVPR ) , 2022 (Oral)
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Learning cross-view features with a Transformer enables optical flow to be solved by directly comparing feature similarities.
High-Resolution Optical Flow from 1D Attention and Correlation
Haofei Xu ,
Jiaolong Yang ,
Jianfei Cai ,
Juyong Zhang ,
Xin Tong
International Conference on Computer Vision (ICCV ) , 2021 (Oral)
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Factorizing 2D optical flow with 1D attention and 1D correlation enables 4K resolution optical flow estimation on standard GPUs.
AANet: Adaptive Aggregation Network for Efficient Stereo Matching
Haofei Xu ,
Juyong Zhang
Computer Vision and Pattern Recognition (CVPR ) , 2020
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A sparse points-based cost aggregation method leads to an efficient and accurate stereo matching architecture without any 3D convolutions.
Invited Talks
DepthSplat: Connecting Gaussian Splatting and Depth, Google DeepMind , hosted by Ben Poole , 2024.10.29
Unifying Flow, Stereo and Depth Estimation [slides] ,
Synced , 2022.12.28
GMFlow: Learning Optical Flow via Global Matching [slides] , Monash University , 2022.04.13
Academic Services
Conference Reviewer: ICCV 2021, CVPR 2022, ECCV 2022, CVPR 2023, NeurIPS 2023, CVPR 2024, ECCV 2024, NeurIPS 2024
Journal Reviewer: TIP, IJCV, TPAMI