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

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 …

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 …

Recent breakthroughs in text-to-4D generation rely on pre-trained text-to-image and text-to-
video models to generate dynamic 3D scenes. However current text-to-4D methods face a …

We present a method that simultaneously addresses the tasks of dynamic scene novel-view
synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

We present a method that simultaneously addresses the tasks of dynamic scene novel-view
synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We …

Remarkable progress has been made in 4D content generation recently. However, existing
methods suffer from long optimization time, lack of motion controllability, and a low level of …

Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD)
community. Recent methods show NeRFs' potential for closed-loop simulation enabling …

Holistic understanding of urban scenes based on RGB images is a challenging yet important
problem. It encompasses understanding both the geometry and appearance to enable novel …