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 …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

Existing state-of-the-art 3D instance segmentation methods perform semantic segmentation
followed by grou**. The hard predictions are made when performing semantic …

We introduce the task of open-vocabulary 3D instance segmentation. Current approaches
for 3D instance segmentation can typically only recognize object categories from a pre …

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 …

How to learn effective features from large-scale point clouds for semantic segmentation has
attracted increasing attention in recent years. Addressing this problem, we propose a …

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 …

To date, various 3D scene understanding tasks still lack practical and generalizable pre-
trained models, primarily due to the intricate nature of 3D scene understanding tasks and …

We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …