Point Cloud
Point clouds are collections of 3D data points representing objects or scenes, primarily used for tasks like 3D reconstruction, object recognition, and autonomous navigation. Current research focuses on improving the efficiency and robustness of point cloud processing, employing techniques like deep learning (e.g., transformers, convolutional neural networks), optimal transport, and Gaussian splatting for tasks such as registration, completion, and compression. These advancements are crucial for applications ranging from robotics and autonomous driving to medical imaging and cultural heritage preservation, enabling more accurate and efficient analysis of complex 3D data.
Papers
Masked Discrimination for Self-Supervised Learning on Point Clouds
Haotian Liu, Mu Cai, Yong Jae Lee
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, Yulan Guo
Upsampling Autoencoder for Self-Supervised Point Cloud Learning
Cheng Zhang, Jian Shi, Xuan Deng, Zizhao Wu
Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion
Xiaopei Wu, Liang Peng, Honghui Yang, Liang Xie, Chenxi Huang, Chengqi Deng, Haifeng Liu, Deng Cai
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
Changfeng Ma, Yang Yang, Jie Guo, Chongjun Wang, Yanwen Guo
Visualizing Global Explanations of Point Cloud DNNs
Hanxiao Tan
3DAC: Learning Attribute Compression for Point Clouds
Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo
AdaSplats: Adaptive Splatting of Point Clouds for Accurate 3D Modeling and Real-time High-Fidelity LiDAR Simulation
Jean Pierre Richa, Jean-Emmanuel Deschaud, François Goulette, Nicolas Dalmasso
Deep Point Cloud Simplification for High-quality Surface Reconstruction
Yuanqi Li, Jianwei Guo, Xinran Yang, Shun Liu, Jie Guo, Xiaopeng Zhang, Yanwen Guo
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
Meida Chen, Qingyong Hu, Zifan Yu, Hugues Thomas, Andrew Feng, Yu Hou, Kyle McCullough, Fengbo Ren, Lucio Soibelman
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
Yan Xia, Qiangqiang Wu, Wei Li, Antoni B. Chan, Uwe Stilla
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation
Robin Wang, Yibo Yang, Dacheng Tao
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap
Yongwei Chen, Zihao Wang, Longkun Zou, Ke Chen, Kui Jia