Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

LiDAR point-cloud segmentation is an important problem for many applications. For large-
scale point cloud segmentation, the de facto method is to project a 3D point cloud to get a …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

We propose to harness the potential of simulation for semantic segmentation of real-world
self-driving scenes in a domain generalization fashion. The segmentation network is trained …

Earlier work demonstrates the promise of deep-learning-based approaches for point cloud
segmentation; however, these approaches need to be improved to be practically useful. To …

We tackle the problem of producing realistic simulations of LiDAR point clouds, the sensor of
preference for most self-driving vehicles. We argue that, by leveraging real data, we can …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …

Intelligent transportation systems (ITSs) have been fueled by the rapid development of
communication technologies, sensor technologies, and the Internet of Things (IoT) …

3D semantic segmentation is one of the key tasks for autonomous driving system. Recently,
deep learning models for 3D semantic segmentation task have been widely researched, but …