activity
20222026
most citedDiffusion Unit: Interpretable Edge Enhancement and Suppression Learning for 3D Point Cloud Segmentation

2 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Interaction-Limited Safe Continuous-Time RL for Dynamical Medical Treatment

Xun Shen, Yuepeng Wang, Akifumi Wachi +13

Dynamic medical treatment requires deciding treatment intensity and intervention timing, while patient states evolve continuously and adverse events may occur between clinical inte…

cs.LG2026

A Unified Benchmark for Dynamic Medical Treatment Reinforcement Learning

Yuepeng Wang, Ken Kawano, Yongqi Zhou +12

Medical treatment recommendation poses several challenges to reinforcement learning (RL): patient physiology evolves in continuous time, measurements and interventions are performe…

cs.CV2023

MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning

Haoyi Xiu, Xin Liu, Weimin Wang +2

Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. While existing methods for normal estimation p…

cs.CV2022★ 2 cited

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,…

cs.CV2022

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…