6 papers
Learning to Generate 4D LiDAR Sequences
Ao Liang, Youquan Liu, Yu Yang +5
While generative world models have advanced video and occupancy-based data synthesis, LiDAR generation remains underexplored despite its importance for accurate 3D perception. Exte…
LiDARCrafter: Dynamic 4D World Modeling from LiDAR Sequences
Ao Liang, Youquan Liu, Yu Yang +5
Generative world models have become essential data engines for autonomous driving, yet most existing efforts focus on videos or occupancy grids, overlooking the unique LiDAR proper…
Perspective-Invariant 3D Object Detection
Ao Liang, Lingdong Kong, Dongyue Lu +4
With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly fo…
TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness
Mu Chen, Wenyu Chen, Mingchuan Yang +5
3D semantic occupancy has rapidly become a research focus in the fields of robotics and autonomous driving environment perception due to its ability to provide more realistic geome…
PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation
Ao Liang, Haiyang Hua, Jian Fang +2
Current Point-based detectors can only learn from the provided points, with limited receptive fields and insufficient global learning capabilities for such targets. In this paper,…
SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism
Ao Liang, Wenyu Chen, Jian Fang +1
The single-stage point-based 3D object detectors have attracted widespread research interest due to their advantages of lightweight and fast inference speed. However, they still fa…