Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

Point cloud analysis is challenging due to irregularity and unordered data structure. To
capture the 3D geometries, prior works mainly rely on exploring sophisticated local …

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 …

Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

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 …

Recently, the advancement of deep learning (DL) in discriminative feature learning from 3-D
LiDAR data has led to rapid development in the field of autonomous driving. However …

Point cloud analysis is very challenging, as the shape implied in irregular points is difficult to
capture. In this paper, we propose RS-CNN, namely, Relation-Shape Convolutional Neural …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …