3 citations · 5 across the 7 of their papers we have counts for
7 papers
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…
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…
Fast convolution kernels on pascal GPU with high memory efficiency
Qiong Chang, Masaki Onishi, Tsutomu Maruyama
The convolution computation is widely used in many fields, especially in CNNs. Because of the rapid growth of the training data in CNNs, GPUs have been used for the acceleration, a…
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…