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
A Critical Analysis of Internal Reliability for Uncertainty Quantification of Dense Image Matching in Multi-view Stereo
Debao Huang, Rongjun Qin
Hamiltonian Dynamics Learning from Point Cloud Observations for Nonholonomic Mobile Robot Control
Abdullah Altawaitan, Jason Stanley, Sambaran Ghosal, Thai Duong, Nikolay Atanasov
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Yiwen Tang, Xianzheng Ma, Jiaming Han, Kexin Chen, Peng Gao, Xianzhi Li, Hongsheng Li, Pheng-Ann Heng
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Shiqi Yang, Yaxing Wang, Joost van de Weijer, Luis Herranz, Shangling Jui, Jian Yang
Robust Point Cloud Processing through Positional Embedding
Jianqiao Zheng, Xueqian Li, Sameera Ramasinghe, Simon Lucey
PointLLM: Empowering Large Language Models to Understand Point Clouds
Runsen Xu, Xiaolong Wang, Tai Wang, Yilun Chen, Jiangmiao Pang, Dahua Lin
PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie Zhou, Jiwen Lu
Decoupled Local Aggregation for Point Cloud Learning
Binjie Chen, Yunzhou Xia, Yu Zang, Cheng Wang, Jonathan Li
SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects
Ning Gao, Ngo Anh Vien, Hanna Ziesche, Gerhard Neumann
MS23D: : A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer
Yongxin Shao, Aihong Tan, Binrui Wang, Tianhong Yan, Zhetao Sun, Yiyang Zhang, Jiaxin Liu
Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning
Ahmed Hatem, Yiming Qian, Yang Wang