1 citations · 1 across the 5 of their papers we have counts for
5 papers
ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning
Song Wang, Zhongdao Wang, Jiawei Yu +4
Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having not…
VEON: Vocabulary-Enhanced Occupancy Prediction
Jilai Zheng, Pin Tang, Zhongdao Wang +4
Perceiving the world as 3D occupancy supports embodied agents to avoid collision with any types of obstacle. While open-vocabulary image understanding has prospered recently, how t…
Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts
Jianhao Li, Tianyu Sun, Zhongdao Wang +7
This paper proposes an algorithm for automatically labeling 3D objects from 2D point or box prompts, especially focusing on applications in autonomous driving. Unlike previous arts…
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Guoqing Wang, Zhongdao Wang, Pin Tang +4
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem. These discriminative methods focus on…
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
Pin Tang, Zhongdao Wang, Guoqing Wang +4
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. Howe…