19 citations · 32 across the 8 of their papers we have counts for
9 papers
TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions
Yixin Sun, Li Li, Wenke E +2
Detecting traversable pathways in unstructured outdoor environments remains a significant challenge for autonomous robots, especially in critical applications such as wide-area sea…
TFDM: Time-Variant Frequency-Based Point Cloud Diffusion with Mamba
Jiaxu Liu, Li Li, Hubert P. H. Shum +1
Diffusion models currently demonstrate impressive performance over various generative tasks. Recent work on image diffusion highlights the strong capabilities of Mamba (state space…
Dur360BEV: A Real-world 360-degree Single Camera Dataset and Benchmark for Bird-Eye View Mapping in Autonomous Driving
Wenke E, Chao Yuan, Li Li +4
We present Dur360BEV, a novel spherical camera autonomous driving dataset equipped with a high-resolution 128-channel 3D LiDAR and a RTK-refined GNSS/INS system, along with a bench…
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…
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
Li Li, Hubert P. H. Shum, Toby P. Breckon
Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervis…