7 citations · 7 across the 8 of their papers we have counts for
10 papers
Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
Henry Che, Jingkang Wang, Yun Chen +3
Urban scene reconstruction from real-world observations has emerged as a powerful tool for self-driving development and testing. While current neural rendering approaches achieve h…
GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
Jiale Shi, Jiarui Hu, Zesong Yang +3
We introduce GaussianZoom, a generative zoom-in 3D reconstruction system with an iterative progressive framework that combines geometry-consistent scene modeling and multi-scale se…
GenAssets: Generating in-the-wild 3D Assets in Latent Space
Ze Yang, Jingkang Wang, Haowei Zhang +3
High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from i…
Flux4D: Flow-based Unsupervised 4D Reconstruction
Jingkang Wang, Henry Che, Yun Chen +4
Reconstructing large-scale dynamic scenes from visual observations is a fundamental challenge in computer vision, with critical implications for robotics and autonomous systems. Wh…
SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Time
Yun Chen, Matthew Haines, Jingkang Wang +5
High-fidelity sensor simulation of light-based sensors such as cameras and LiDARs is critical for safe and accurate autonomy testing. Neural radiance field (NeRF)-based methods tha…
G3R: Gradient Guided Generalizable Reconstruction
Yun Chen, Jingkang Wang, Ze Yang +2
Large scale 3D scene reconstruction is important for applications such as virtual reality and simulation. Existing neural rendering approaches (e.g., NeRF, 3DGS) have achieved real…
UniCal: Unified Neural Sensor Calibration
Ze Yang, George Chen, Haowei Zhang +5
Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fidu…
LightSim: Neural Lighting Simulation for Urban Scenes
Ava Pun, Gary Sun, Jingkang Wang +5
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 du…
Real-Time Neural Rasterization for Large Scenes
Jeffrey Yunfan Liu, Yun Chen, Ze Yang +3
We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes. Existing neural rendering methods generate realistic results, but primarily work for sma…
Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
Lunjun Zhang, Yuwen Xiong, Ze Yang +3
Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling wo…