Implicit 3D scene reconstruction using deep learning towards efficient collision understanding in autonomous driving
It addresses the need for more accurate 3D obstacle mapping in dense traffic conditions for autonomous vehicles, though it appears incremental as it builds on existing implicit reconstruction methods.
This research tackles the problem of 3D scene reconstruction for collision detection in autonomous driving by developing a learning-based method using LiDAR data and deep neural networks to build static Signed Distance Function maps, with preliminary results indicating significant enhancement in collision detection performance in congested environments.
In crowded urban environments where traffic is dense, current technologies struggle to oversee tight navigation, but surface-level understanding allows autonomous vehicles to safely assess proximity to surrounding obstacles. 3D or 2D scene mapping of the surrounding objects is an essential task in addressing the above problem. Despite its importance in dense vehicle traffic conditions, 3D scene reconstruction of object shapes with higher boundary level accuracy is not yet entirely considered in current literature. The sign distance function represents any shape through parameters that calculate the distance from any point in space to the closest obstacle surface, making it more efficient in terms of storage. In recent studies, researchers have started to formulate problems with Implicit 3D reconstruction methods in the autonomous driving domain, highlighting the possibility of using sign distance function to map obstacles effectively. This research addresses this gap by developing a learning-based 3D scene reconstruction methodology that leverages LiDAR data and a deep neural network to build a the static Signed Distance Function (SDF) maps. Unlike traditional polygonal representations, this approach has the potential to map 3D obstacle shapes with more boundary-level details. Our preliminary results demonstrate that this method would significantly enhance collision detection performance, particularly in congested and dynamic environments.