2 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
Yahao Shi, Yanmin Wu, Chenming Wu +7
This paper presents a 3D Gaussian Inverse Rendering (GIR) method, employing 3D Gaussian representations to effectively factorize the scene into material properties, light, and geom…
DGNR: Density-Guided Neural Point Rendering of Large Driving Scenes
Zhuopeng Li, Chenming Wu, Liangjun Zhang +1
Despite the recent success of Neural Radiance Field (NeRF), it is still challenging to render large-scale driving scenes with long trajectories, particularly when the rendering qua…
Digging into Depth Priors for Outdoor Neural Radiance Fields
Chen Wang, Jiadai Sun, Lina Liu +5
Neural Radiance Fields (NeRF) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance…
MapNeRF: Incorporating Map Priors into Neural Radiance Fields for Driving View Simulation
Chenming Wu, Jiadai Sun, Zhelun Shen +1
Simulating camera sensors is a crucial task in autonomous driving. Although neural radiance fields are exceptional at synthesizing photorealistic views in driving simulations, they…
Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores
Shukai Liu, Chenming Wu, Ying Li +1
Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of a large amount of inte…
LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection
Jin Fang, Dingfu Zhou, Jingjing Zhao +4
Over the past few years, there has been remarkable progress in research on 3D point clouds and their use in autonomous driving scenarios has become widespread. However, deep learni…