1 code implementation • 11 Apr 2024 • Jiang Wu, Rui Li, Haofei Xu, Wenxun Zhao, Yu Zhu, Jinqiu Sun, Yanning Zhang
More specifically, we correspond and propagate adjacent costs to the reference pixel by leveraging the local geometric smoothness in conjunction with surface normals.
1 code implementation • 21 Mar 2024 • Yuedong Chen, Haofei Xu, Chuanxia Zheng, Bohan Zhuang, Marc Pollefeys, Andreas Geiger, Tat-Jen Cham, Jianfei Cai
We propose MVSplat, an efficient feed-forward 3D Gaussian Splatting model learned from sparse multi-view images.
Ranked #1 on Generalizable Novel View Synthesis on ACID
1 code implementation • 7 Dec 2023 • Haofei Xu, Anpei Chen, Yuedong Chen, Christos Sakaridis, Yulun Zhang, Marc Pollefeys, Andreas Geiger, Fisher Yu
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).
1 code implementation • 24 Apr 2023 • Yuedong Chen, Haofei Xu, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, Jianfei Cai
The key to our approach lies in the explicitly modeled correspondence matching information, so as to provide the geometry prior to the prediction of NeRF color and density for volume rendering.
1 code implementation • 10 Nov 2022 • Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, Fisher Yu, DaCheng Tao, Andreas Geiger
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.
Ranked #1 on Optical Flow Estimation on Sintel-clean
4 code implementations • CVPR 2022 • Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, DaCheng Tao
Learning-based optical flow estimation has been dominated with the pipeline of cost volume with convolutions for flow regression, which is inherently limited to local correlations and thus is hard to address the long-standing challenge of large displacements.
Ranked #8 on Optical Flow Estimation on Spring
1 code implementation • ICCV 2021 • Haofei Xu, Jiaolong Yang, Jianfei Cai, Juyong Zhang, Xin Tong
Optical flow is inherently a 2D search problem, and thus the computational complexity grows quadratically with respect to the search window, making large displacements matching infeasible for high-resolution images.
1 code implementation • CVPR 2021 • Wanquan Feng, Juyong Zhang, Hongrui Cai, Haofei Xu, Junhui Hou, Hujun Bao
Learning non-rigid registration in an end-to-end manner is challenging due to the inherent high degrees of freedom and the lack of labeled training data.
1 code implementation • CVPR 2020 • Haofei Xu, Juyong Zhang
Despite the remarkable progress made by learning based stereo matching algorithms, one key challenge remains unsolved.
Ranked #1 on Scene Flow Estimation on Scene Flow
1 code implementation • 26 Feb 2019 • Haofei Xu, Jianmin Zheng, Jianfei Cai, Juyong Zhang
In this paper, we propose a new learning based method consisting of DepthNet, PoseNet and Region Deformer Networks (RDN) to estimate depth from unconstrained monocular videos without ground truth supervision.