Many applications in robotics and human-computer interaction can benefit from
understanding 3D motion of points in a dynamic environment, widely noted as scene flow …

Automation in point cloud data processing is central in knowledge discovery within decision-
making systems. The definition of relevant features is often key for segmentation and …

Equivariance has gained strong interest as a desirable network property that inherently
ensures robust generalization. However, when dealing with complex systems such as …

Point clouds have garnered increasing research attention and found numerous practical
applications. However, many of these applications, such as autonomous driving and robotic …

A considerable amount of annotated training data is necessary to achieve state-of-the-art
performance in perception tasks using point clouds. Unlike RGB-images, LiDAR point …

We propose a new approach called LiDAR-Flow to robustly estimate a dense scene flow by
fusing a sparse LiDAR with stereo images. We take the advantage of the high accuracy of …

Object detection and motion parameters estimation are crucial tasks for self-driving vehicle
safe navigation in a complex urban environment. In this work we propose a novel real-time …

Autonomous vehicles require an accurate understanding of the underlying motion of their
surroundings. Traditionally this understanding is acquired using optical flow algorithms on …

To empower an autonomous robot to perform long-term navigation in a given area, a
concurrent localization and map update algorithm is required. In this paper, we tackle this …

Flow estimation on 3D point clouds is a challenging problem in the field of computer vision,
which has great significance in many areas, such as autonomous driving and human …