12 citations · 20 across the 7 of their papers we have counts for
7 papers
L2RSI: Cross-view LiDAR-based Place Recognition for Large-scale Urban Scenes via Remote Sensing Imagery
Ziwei Shi, Xiaoran Zhang, Wenjing Xu +4
We tackle the challenge of LiDAR-based place recognition, which traditionally depends on costly and time-consuming prior 3D maps. To overcome this, we first construct LiRSI-XA data…
A New Adversarial Perspective for LiDAR-based 3D Object Detection
Shijun Zheng, Weiquan Liu, Yu Guo +3
Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in comple…
E2PNet: Event to Point Cloud Registration with Spatio-Temporal Representation Learning
Xiuhong Lin, Changjie Qiu, Zhipeng Cai +6
Event cameras have emerged as a promising vision sensor in recent years due to their unparalleled temporal resolution and dynamic range. While registration of 2D RGB images to 3D p…
Decoupled Local Aggregation for Point Cloud Learning
Binjie Chen, Yunzhou Xia, Yu Zang +2
The unstructured nature of point clouds demands that local aggregation be adaptive to different local structures. Previous methods meet this by explicitly embedding spatial relatio…
DSMNet: Deep High-precision 3D Surface Modeling from Sparse Point Cloud Frames
Changjie Qiu, Zhiyong Wang, Xiuhong Lin +3
Existing point cloud modeling datasets primarily express the modeling precision by pose or trajectory precision rather than the point cloud modeling effect itself. Under this deman…
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network
Weiquan Liu, Minghao Liu, Shijun Zheng +1
Although 3D point cloud classification neural network models have been widely used, the in-depth interpretation of the activation of the neurons and layers is still a challenge. We…
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Xu Yan, Xiaoliang Fan, Peizhen Yang +5
Representation learning on temporal interaction graphs (TIG) is to model complex networks with the dynamic evolution of interactions arising in a broad spectrum of problems. Existi…