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
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation
Shaobo Xia, Jun Yue, Kacper Kania, Leyuan Fang, Andrea Tagliasacchi, Kwang Moo Yi, Weiwei Sun
Adaptive Annotation Distribution for Weakly Supervised Point Cloud Semantic Segmentation
Zhiyi Pan, Nan Zhang, Wei Gao, Shan Liu, Ge Li
M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking
Jiaming Liu, Yue Wu, Maoguo Gong, Qiguang Miao, Wenping Ma, Can Qin
PCRDiffusion: Diffusion Probabilistic Models for Point Cloud Registration
Yue Wu, Yongzhe Yuan, Xiaolong Fan, Xiaoshui Huang, Maoguo Gong, Qiguang Miao
Automated Multimodal Data Annotation via Calibration With Indoor Positioning System
Ryan Rubel, Andrew Dudash, Mohammad Goli, James O'Hara, Karl Wunderlich
Novel class discovery meets foundation models for 3D semantic segmentation
Luigi Riz, Cristiano Saltori, Yiming Wang, Elisa Ricci, Fabio Poiesi
Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method
Hongyu Huang, Guoji Tian, Chongcheng Chen
PointMoment:Mixed-Moment-based Self-Supervised Representation Learning for 3D Point Clouds
Xin Cao, Xinxin Han, Yifan Wang, Mengna Yang, Kang Li
PointJEM: Self-supervised Point Cloud Understanding for Reducing Feature Redundancy via Joint Entropy Maximization
Xin Cao, Huan Xia, Xinxin Han, Yifan Wang, Kang Li, Linzhi Su
DiffusionPCR: Diffusion Models for Robust Multi-Step Point Cloud Registration
Zhi Chen, Yufan Ren, Tong Zhang, Zheng Dang, Wenbing Tao, Sabine Süsstrunk, Mathieu Salzmann
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More
Jan Schuchardt, Yan Scholten, Stephan Günnemann
DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors
Xiaze Zhang, Ziheng Ding, Qi Jing, Yuejie Zhang, Wenchao Ding, Rui Feng
Zero-Shot Point Cloud Registration
Weijie Wang, Guofeng Mei, Bin Ren, Xiaoshui Huang, Fabio Poiesi, Luc Van Gool, Nicu Sebe, Bruno Lepri