6 citations · 11 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
Reconstructing Objects in-the-wild for Realistic Sensor Simulation
Ze Yang, Sivabalan Manivasagam, Yun Chen +3
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing.…
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
Jingkang Wang, Sivabalan Manivasagam, Yun Chen +5
Realistic simulation is key to enabling safe and scalable development of % self-driving vehicles. A core component is simulating the sensors so that the entire autonomy system can…
UniSim: A Neural Closed-Loop Sensor Simulator
Ze Yang, Yun Chen, Jingkang Wang +4
Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be col…
RBGNet: Ray-based Grouping for 3D Object Detection
Haiyang Wang, Shaoshuai Shi, Ze Yang +5
As a fundamental problem in computer vision, 3D object detection is experiencing rapid growth. To extract the point-wise features from the irregularly and sparsely distributed poin…
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Ze Yang, Shenlong Wang, Sivabalan Manivasagam +5
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.…