37 citations · 65 across the 5 of their papers we have counts for
9 papers · 1 filter
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…
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Yun Chen, Frieda Rong, Shivam Duggal +6
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photor…
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Ming Liang, Bin Yang, Yun Chen +2
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that rea…