Abstract 3D scene flow estimation aims to estimate point-wise motions between two
consecutive frames of point clouds. Superpoints, ie, points with similar geometric features …

In this work, we focus on scene flow learning on point clouds in a self-supervised manner. A
real-world scene can be well modeled as a collection of rigidly moving parts, therefore its …

Understanding the motion behavior of dynamic environments is vital for autonomous driving,
leading to increasing attention in class-agnostic motion prediction in LiDAR point clouds …

Since estimating scene flow from point clouds is challenging, some methods involve the
robust Transformer. However, there are two problems with these methods:(1) Dense …

In this paper, we present a new self-supervised scene flow estimation approach for a pair of
consecutive point clouds. The key idea of our approach is to represent discrete point clouds …

Sequential point clouds acquired by light detection and ranging (LiDAR) technology provide
accurate spatial information for environmental sensing. However, semantic segmentation of …

Estimating scene flow for point clouds is one of the key problems in 3D scene understanding
and autonomous driving. Recently the point transformer architecture has become a popular …

In this article, we present an efficient coding scheme for LiDAR point cloud maps. As a point
cloud map consists of numerous single scans spliced together, by recording the time stamp …

In this article, we investigate self-supervised 3D scene flow estimation and class-agnostic
motion prediction on point clouds. A realistic scene can be well modeled as a collection of …

Despite the impressive results achieved by deep learning based 3D reconstruction, the
techniques of directly learning to model 4D human captures with detailed geometry have …