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 …

This paper aims to tackle the problem of modeling dynamic urban streets for autonomous
driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to …

Scene simulation in autonomous driving has gained significant attention because of its huge
potential for generating customized data. However existing editable scene simulation …

Reconstruction and intrinsic decomposition of scenes from captured imagery would enable
many applications such as relighting and virtual object insertion. Recent NeRF based …

Because of the diversity in lighting environments, existing illumination estimation techniques
have been designed explicitly on indoor or outdoor environments. Methods have focused …

The systematic evaluation and understanding of computer vision models under varying
conditions require large amounts of data with comprehensive and customized labels which …

This work explores the use of 3D generative models to synthesize training data for 3D vision
tasks. The key requirements of the generative models are that the generated data should be …

A major challenge in monocular 3D object detection is the limited diversity and quantity of
objects in real datasets. While augmenting real scenes with virtual objects holds promise to …

Reconstructing objects from real world data and rendering them at novel views is critical to
bringing realism, diversity and scale to simulation for robotics training and testing. In this …

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 …