28 citations · 40 across the 7 of their papers we have counts for
8 papers
Semi-supervised 3D Object Detection with PatchTeacher and PillarMix
Xiaopei Wu, Liang Peng, Liang Xie +6
Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to g…
Neural Collapse Inspired Federated Learning with Non-iid Data
Chenxi Huang, Liang Xie, Yibo Yang +3
One of the challenges in federated learning is the non-independent and identically distributed (non-iid) characteristics between heterogeneous devices, which cause significant diff…
General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment
Liang Xie, Yibo Yang, Wenxiao Wang +4
Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformati…
Neural Collapse Inspired Attraction-Repulsion-Balanced Loss for Imbalanced Learning
Liang Xie, Yibo Yang, Deng Cai +1
Class imbalance distribution widely exists in real-world engineering. However, the mainstream optimization algorithms that seek to minimize error will trap the deep learning model…
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Xiaopei Wu, Liang Peng, Honghui Yang +5
Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds. Many multi-modal methods are proposed to alleviate this issue, while different represen…
X-view: Non-egocentric Multi-View 3D Object Detector
Liang Xie, Guodong Xu, Deng Cai +1
3D object detection algorithms for autonomous driving reason about 3D obstacles either from 3D birds-eye view or perspective view or both. Recent works attempt to improve the detec…
Boundary-Aware Dense Feature Indicator for Single-Stage 3D Object Detection from Point Clouds
Guodong Xu, Wenxiao Wang, Zili Liu +4
3D object detection based on point clouds has become more and more popular. Some methods propose localizing 3D objects directly from raw point clouds to avoid information loss. How…
PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module
Liang Xie, Chao Xiang, Zhengxu Yu +4
LIDAR point clouds and RGB-images are both extremely essential for 3D object detection. So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data…