In this paper, we propose an unsupervised domain adaptation method for deep point cloud
representation learning. To model the internal structures in target point clouds, we first …

Self-supervised learning can extract representations of good quality from solely unlabeled
data, which is appealing for point cloud videos due to their high labelling cost. In this paper …

Recently, the community has made tremendous progress in develo** effective methods
for point cloud video understanding that learn from massive amounts of labeled data …

We propose a unified point cloud video self-supervised learning framework for object-centric
and scene-centric data. Previous methods commonly conduct representation learning at the …

Observing that it is still a challenging task to deploy 3D action recognition methods in real-
world scenarios, in this work, we investigate the accuracy-efficiency trade-off for 3D action …

Autonomous systems need to process large-scale sparse and irregular point clouds with
limited compute resources. Consequently it is essential to develop LiDAR perception …

We present a new self-supervised paradigm on point cloud sequence understanding.
Inspired by the discriminative and generative self-supervised methods, we design two tasks …

This paper investigates the problem of temporally interpolating dynamic 3D point clouds with
large non-rigid deformation. We formulate the problem as estimation of point-wise …

This paper focuses on exploring distinctive spatio-temporal representation in a self-
supervised manner for group activity recognition. Firstly, previous networks treat spatial-and …

The existing end-to-end optimized 3D action recognition methods often suffer from high
computational costs. Observing that different frames and different points in point cloud …