Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene
representation has taken the field of Computer Vision by storm. As a novel view synthesis …

The grid-centric perception is a crucial field for mobile robot perception and navigation.
Nonetheless, the grid-centric perception is less prevalent than object-centric perception as …

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 …

We present DrivingGaussian an efficient and effective framework for surrounding dynamic
autonomous driving scenes. For complex scenes with moving objects we first sequentially …

We extend neural radiance fields (NeRFs) to dynamic large-scale urban scenes. Prior work
tends to reconstruct single video clips of short durations (up to 10 seconds). Two reasons …

The advancement of real-time 3D scene reconstruction and novel view synthesis has been
significantly propelled by 3D Gaussian Splatting (3DGS). However, effectively training large …

We use neural radiance fields (NeRFs) to build interactive 3D environments from large-scale
visual captures spanning buildings or even multiple city blocks collected primarily from …

We present an algorithm for reconstructing the radiance field of a large-scale scene from a
single casually captured video. The task poses two core challenges. First, most existing …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …