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I am a PhD student at ETH Zurich and University of Tübingen, supervised by Marc Pollefeys and Andreas Geiger. I was very fortunate to work with several amazing supervisors. I spent a wonderful time working remotely with Jianfei Cai and Hamid Rezatofighi at Monash University, Australia, prior to starting my PhD. I obtained a master's degree at University of Science and Technology of China (USTC) supervised by Juyong Zhang. During my master's, I exchanged at Nanyang Technological University (NTU), Singapore, where I was supervised by Jianfei Cai and Jianmin Zheng. I also interned at Microsoft Research Asia (MSRA), where I was mentored by Jiaolong Yang and Xin Tong. |
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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. |
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Haofei Xu, Songyou Peng, Fangjinhua Wang, Hermann Blum, Daniel Barath, Andreas Geiger, Marc Pollefeys project page / code DepthSplat enables cross-task interactions between Gaussian splatting and depth estimation. |
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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) project page / code A cost volume representation for efficiently predicting 3D Gaussians from sparse multi-view images in a single forward pass. |
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Anpei Chen, Haofei Xu, Stefano Esposito, Siyu Tang, Andreas Geiger European Conference on Computer Vision (ECCV), 2024 project page / code A feed-forward 2DGS model trained in two days using four GPUs. |
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Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, Dacheng Tao, Andreas Geiger IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2023 project page / slides / video(cn) / colab / demo / code A unified dense correspondence matching formulation enables three motion and 3D perception tasks to be solved with a unified model. |
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Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Dacheng Tao Computer Vision and Pattern Recognition (CVPR), 2022 (Oral) slides / video(cn) / poster / code Learning cross-view features with a Transformer enables optical flow to be solved by directly comparing feature similarities. |
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Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong International Conference on Computer Vision (ICCV), 2021 (Oral) code Factorizing 2D optical flow with 1D attention and 1D correlation enables 4K resolution optical flow estimation on standard GPUs. |
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Haofei Xu, Juyong Zhang Computer Vision and Pattern Recognition (CVPR), 2020 code A sparse points-based cost aggregation method leads to an efficient and accurate stereo matching architecture without any 3D convolutions. |
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