19 citations · 32 across the 8 of their papers we have counts for
5 papers · 1 filter
On Deep Learning for Geometric and Semantic Scene Understanding Using On-Vehicle 3D LiDAR
Li Li
3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and autonomous driving. Geometric and semantic scene understanding, involving 3D point cloud…
TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training
Li Li, Tanqiu Qiao, Hubert P. H. Shum +1
3D point clouds are essential for perceiving outdoor scenes, especially within the realm of autonomous driving. Recent advances in 3D LiDAR Object Detection focus primarily on the…
RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentation
Li Li, Hubert P. H. Shum, Toby P. Breckon
3D point clouds play a pivotal role in outdoor scene perception, especially in the context of autonomous driving. Recent advancements in 3D LiDAR segmentation often focus intensely…
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications
Li Li, Khalid N. Ismail, Hubert P. H. Shum +1
We present DurLAR, a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery, as well as a sample benchmark task using depth esti…
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
Jiahao Sun, Chunmei Qing, Xiang Xu +10
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on d…