Autonomous driving relies heavily on computer vision to guarantee safe driving. It involves solving many important tasks such as object detection, scene segmentation, motion prediction, and ego-motion calculation – all are important for safe planning in the autonomous driving task. While many academic works have focused on using 2D images to perform perception, it is widely agreed that adding other modalities, such as 3D LiDAR data, can improve scene understanding and safety.
Using 3D information for autonomous driving has its unique challenges. A LiDAR reacts differently than a camera to different weather conditions, and there are challenges relating to its data annotation. The data is not represented on a grid as is the case with 2D images, therefore, a dedicated effort is required for processing the 3D data.
The Innoviz and NVIDIA workshop will discuss the challenges and advantages in performing 3D perception for autonomous driving, and the recent trends in the field. The various workshop lectures will be led by leading experts from the academy and industry.
The workshop includes a self-supervised learning for LiDAR challenge with data from Innoviz’s state-of-the-art InnovizTwo LiDAR sensor.