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
PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion
Yi Zhong, Weize Quan, Dong-ming Yan, Jie Jiang, Yingmei Wei
Digging into Intrinsic Contextual Information for High-fidelity 3D Point Cloud Completion
Jisheng Chu, Wenrui Li, Xingtao Wang, Kanglin Ning, Yidan Lu, Xiaopeng Fan
Position-aware Guided Point Cloud Completion with CLIP Model
Feng Zhou, Qi Zhang, Ju Dai, Lei Li, Qing Fan, Junliang Xing
Implicit Neural Compression of Point Clouds
Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao, Zhaoyang Zhang, Dusit Niyato
Rate-Distortion Optimized Skip Coding of Region Adaptive Hierarchical Transform Coefficients for MPEG G-PCC
Zehan Wang, Yuxuan Wei, Hui Yuan, Wei Zhang, Peng Li
AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration
Jiong Lin, Lechen Zhang, Kwansoo Lee, Jialong Ning, Judah Goldfeder, Hod Lipson