14 citations · 45 across the 15 of their papers we have counts for
15 papers
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…
Deep Feedback Inverse Problem Solver
Wei-Chiu Ma, Shenlong Wang, Jiayuan Gu +3
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn…