217 citations · 687 across the 55 of their papers we have counts for
6 papers · 2 filters
Hausdorff Point Convolution with Geometric Priors
Pengdi Huang, Liqiang Lin, Fuyou Xue +3
Without a shape-aware response, it is hard to characterize the 3D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of Hausdor…
On Learning the Right Attention Point for Feature Enhancement
Liqiang Lin, Pengdi Huang, Chi-Wing Fu +3
We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior works, which wer…
SymmetryNet: Learning to Predict Reflectional and Rotational Symmetries of 3D Shapes from Single-View RGB-D Images
Yifei Shi, Junwen Huang, Hongjia Zhang +3
We study the problem of symmetry detection of 3D shapes from single-view RGB-D images, where severely missing data renders geometric detection approach infeasible. We propose an en…
PIE-NET: Parametric Inference of Point Cloud Edges
Xiaogang Wang, Yuelang Xu, Kai Xu +4
We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i.e.,lines,…
MLCVNet: Multi-Level Context VoteNet for 3D Object Detection
Qian Xie, Yu-Kun Lai, Jing Wu +4
In this paper, we address the 3D object detection task by capturing multi-level contextual information with the self-attention mechanism and multi-scale feature fusion. Most existi…
Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
Dengsheng Chen, Jun Li, Zheng Wang +1
We present a novel approach to category-level 6D object pose and size estimation. To tackle intra-class shape variations, we learn canonical shape space (CASS), a unified represent…