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
Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification
Diogo Lavado, Cláudia Soares, Alessandra Micheletti, Giovanni Bocchi, Alex Coronati, Manuel Silva, Patrizio Frosini
Marking anything: application of point cloud in extracting video target features
Xiangchun Xu
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Tao Ma, Xuemeng Yang, Hongbin Zhou, Xin Li, Botian Shi, Junjie Liu, Yuchen Yang, Zhizheng Liu, Liang He, Yu Qiao, Yikang Li, Hongsheng Li
3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review
Omar Elharrouss, Kawther Hassine, Ayman Zayyan, Zakariyae Chatri, Noor almaadeed, Somaya Al-Maadeed, Khalid Abualsaud
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization
Eduardo R. Corral-Soto, Alaap Grandhi, Yannis Y. He, Mrigank Rochan, Bingbing Liu