Image segmentation is a key task in computer vision and image processing with important
applications such as scene understanding, medical image analysis, robotic perception …

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

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

Unlike images which are represented in regular dense grids, 3D point clouds are irregular
and unordered, hence applying convolution on them can be difficult. In this paper, we extend …

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 has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the
point clouds to 2D space and then process them via 2D convolution. Although this …

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 …

In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud.
Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors …

We propose a novel deep learning-based framework to tackle the challenge of semantic
segmentation of large-scale point clouds of millions of points. We argue that the organization …