2 citations · 7 across the 11 of their papers we have counts for
11 papers
L2D2-GS: Learning to Densify for Feedforward Dynamic Gaussian Scene Reconstruction
Zetian Song, Chenming Wu, Junnan Liu +6
High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Gaussian Splatting (3DGS) has…
TransBridge: Boost 3D Object Detection by Scene-Level Completion with Transformer Decoder
Qinghao Meng, Chenming Wu, Liangjun Zhang +1
3D object detection is essential in autonomous driving, providing vital information about moving objects and obstacles. Detecting objects in distant regions with only a few LiDAR p…
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
Zhelun Shen, Zhuo Li, Chenming Wu +4
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their succes…
HERO-SLAM: Hybrid Enhanced Robust Optimization of Neural SLAM
Zhe Xin, Yufeng Yue, Liangjun Zhang +1
Simultaneous Localization and Mapping (SLAM) is a fundamental task in robotics, driving numerous applications such as autonomous driving and virtual reality. Recent progress on neu…
HO-Gaussian: Hybrid Optimization of 3D Gaussian Splatting for Urban Scenes
Zhuopeng Li, Yilin Zhang, Chenming Wu +2
The rapid growth of 3D Gaussian Splatting (3DGS) has revolutionized neural rendering, enabling real-time production of high-quality renderings. However, the previous 3DGS-based met…
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…