3 citations · 5 across the 4 of their papers we have counts for
4 papers
Diffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation
Haoyi Xiu, Xin Liu, Weimin Wang +4
3D point clouds are discrete samples of continuous surfaces which can be used for various applications. However, the lack of true connectivity information, i.e., edge information,…
Enhancing Local Geometry Learning for 3D Point Cloud via Decoupling Convolution
Haoyi Xiu, Xin Liu, Weimin Wang +4
Modeling the local surface geometry is challenging in 3D point cloud understanding due to the lack of connectivity information. Most prior works model local geometry using various…
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding
Haoyi Xiu, Xin Liu, Weimin Wang +4
Learning point clouds is challenging due to the lack of connectivity information, i.e., edges. Although existing edge-aware methods can improve the performance by modeling edges, h…
Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention
Haoyi Xiu, Xin Liu, Weimin Wang +4
We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis th…