28 citations · 48 across the 4 of their papers we have counts for
6 papers · 1 filter
LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation
Zeyu Hu, Xuyang Bai, Runze Zhang +4
We propose LiDAL, a novel active learning method for 3D LiDAR semantic segmentation by exploiting inter-frame uncertainty among LiDAR frames. Our core idea is that a well-trained m…
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Xuyang Bai, Zeyu Hu, Xinge Zhu +4
LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against i…
Learning to Match Features with Seeded Graph Matching Network
Hongkai Chen, Zixin Luo, Jiahui Zhang +5
Matching local features across images is a fundamental problem in computer vision. Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching Network, a graph…
VMNet: Voxel-Mesh Network for Geodesic-Aware 3D Semantic Segmentation
Zeyu Hu, Xuyang Bai, Jiaxiang Shang +6
In recent years, sparse voxel-based methods have become the state-of-the-arts for 3D semantic segmentation of indoor scenes, thanks to the powerful 3D CNNs. Nevertheless, being obl…
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Xuyang Bai, Zixin Luo, Lei Zhou +5
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning…
JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
Zeyu Hu, Mingmin Zhen, Xuyang Bai +2
Semantic segmentation and semantic edge detection can be seen as two dual problems with close relationships in computer vision. Despite the fast evolution of learning-based 3D sema…