4 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
LightLoc++: Sensor-Robust Representation Learning for Efficient Outdoor LiDAR Localization
Wen Li, Shangshu Yu, Dunqiang Liu +5
Scene coordinate regression (SCR) achieves strong performance in outdoor LiDAR localization, but it usually requires scene-specific training that can take days, limiting practical…
CVKD-UDA: Cross-View Knowledge Distillation for 3D Unsupervised Domain Adaptive Segmentation
Zhimin Yuan, Ming Cheng, Shangshu Yu +4
3D unsupervised domain adaptive (UDA) segmentation mitigates the high cost of manual annotations of the new domain data. Self-training has emerged as the dominant approach in this…
LightLoc: Learning Outdoor LiDAR Localization at Light Speed
Wen Li, Chen Liu, Shangshu Yu +5
Scene coordinate regression achieves impressive results in outdoor LiDAR localization but requires days of training. Since training needs to be repeated for each new scene, long tr…
GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding
Changshuo Wang, Meiqing Wu, Siew-Kei Lam +5
Despite the significant advancements in pre-training methods for point cloud understanding, directly capturing intricate shape information from irregular point clouds without relia…
Review: deep learning on 3D point clouds
Saifullahi Aminu Bello, Shangshu Yu, Cheng Wang
Point cloud is point sets defined in 3D metric space. Point cloud has become one of the most significant data format for 3D representation. Its gaining increased popularity as a re…