most citedEnhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention

3 citations · 5 across the 7 of their papers we have counts for

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cs.CV2022

Real-Time High-Quality Stereo Matching System on a GPU

Qiong Chang, Tsutomu Maruyama

In this paper, we propose a low error rate and real-time stereo vision system on GPU. Many stereo vision systems on GPU have been proposed to date. In those systems, the error rate…

cs.CV2022

Efficient stereo matching on embedded GPUs with zero-means cross correlation

Qiong Chang, Aolong Zha, Weimin Wang +5

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usuall…

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…

cs.CV2022

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…

cs.CV2022★ 3 cited

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…