Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

Existing image editing tools, while powerful, typically disregard the underlying 3D geometry
from which the image is projected. As a result, edits made using these tools may become …

We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes.
Existing fast neural rendering methods generate realistic results, but primarily work for small …

Abstract We address outdoor Neural Radiance Fields (NeRF) with LiDAR maps. Existing
NeRF methods usually require specially collected hypersampled source views and do not …

Pedestrian collision avoidance is a crucial task in the development and democratization of
autonomous vehicles. The aim of this review is to provide an accessible overview of the …

Different outdoor illumination conditions drastically alter the appearance of urban scenes,
and they can harm the performance of image-based robot perception systems if not seen …

Recently, with the rapid development in vehicle-to-infrastructure communication
technologies, the infrastructure-based, roadside perception system for cooperative driving …

Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse
sensor data accurately for autonomy. Traditional calibration methods typically leverage …

Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to
ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate …

Large scale 3D scene reconstruction is important for applications such as virtual reality and
simulation. Existing neural rendering approaches (eg, NeRF, 3DGS) have achieved realistic …