67 citations · 120 across the 12 of their papers we have counts for
7 papers · 1 filter
Learning a Task-specific Descriptor for Robust Matching of 3D Point Clouds
Zhiyuan Zhang, Yuchao Dai, Bin Fan +2
Existing learning-based point feature descriptors are usually task-agnostic, which pursue describing the individual 3D point clouds as accurate as possible. However, the matching t…
CU-Net: LiDAR Depth-Only Completion With Coupled U-Net
Yufei Wang, Yuchao Dai, Qi Liu +3
LiDAR depth-only completion is a challenging task to estimate dense depth maps only from sparse measurement points obtained by LiDAR. Even though the depth-only methods have been w…
Searching Dense Point Correspondences via Permutation Matrix Learning
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +2
Although 3D point cloud data has received widespread attentions as a general form of 3D signal expression, applying point clouds to the task of dense correspondence estimation betw…
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
Jiadai Sun, Yuchao Dai, Xianjing Zhang +4
Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path p…
Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis
Xiang Guo, Guanying Chen, Yuchao Dai +4
Recently, Neural Radiance Fields (NeRF) is revolutionizing the task of novel view synthesis (NVS) for its superior performance. In this paper, we propose to synthesize dynamic scen…
VRNet: Learning the Rectified Virtual Corresponding Points for 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +2
3D point cloud registration is fragile to outliers, which are labeled as the points without corresponding points. To handle this problem, a widely adopted strategy is to estimate t…
A Representation Separation Perspective to Correspondences-free Unsupervised 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
3D point cloud registration in remote sensing field has been greatly advanced by deep learning based methods, where the rigid transformation is either directly regressed from the t…