131 citations · 225 across the 5 of their papers we have counts for
5 papers
PointCNN++: Performant Convolution on Native Points
Lihan Li, Haofeng Zhong, Rui Bu +4
Existing convolutional learning methods for 3D point cloud data are divided into two paradigms: point-based methods that preserve geometric precision but often face performance cha…
DO-Conv: Depthwise Over-parameterized Convolutional Layer
Jinming Cao, Yangyan Li, Mingchao Sun +5
Convolutional layers are the core building blocks of Convolutional Neural Networks (CNNs). In this paper, we propose to augment a convolutional layer with an additional depthwise c…
Mutual Information Maximization in Graph Neural Networks
Xinhan Di, Pengqian Yu, Rui Bu +1
A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme t…
PointCNN: Convolution On -Transformed Points
Yangyan Li, Rui Bu, Mingchao Sun +3
We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…