Machine Learning (ML) applications on Light Detection And Ranging (LiDAR) data have
provided promising results and thus this topic has been widely addressed in the literature …

Graph convolutional networks (GCNs) have a strong ability to learn graph representation
and have achieved good performance in a range of applications, including social …

Abstract We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the convolution …

Abstract We introduce the SE (3)-Transformer, a variant of the self-attention module for 3D
point-clouds, which is equivariant under continuous 3D roto-translations. Equivariance is …

Abstract We present Kernel Point Convolution (KPConv), a new design of point convolution,
ie that operates on point clouds without any intermediate representation. The convolution …

Deep learning techniques for point cloud data have demonstrated great potentials in solving
classical problems in 3D computer vision such as 3D object classification and segmentation …

Abstract Convolutional Neural Networks (CNNs) achieve impressive performance in a wide
variety of fields. Their success benefited from a massive boost when very deep CNN models …

Deep learning with 3D data has progressed significantly since the introduction of
convolutional neural networks that can handle point order ambiguity in point cloud data …

Multi-view projection methods have demonstrated their ability to reach state-of-the-art
performance on 3D shape recognition. Those methods learn different ways to aggregate …

Due to the sparsity and irregularity of the point cloud data, methods that directly consume
points have become popular. Among all point-based models, graph convolutional networks …