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
An Evaluation of Three Distance Measurement Technologies for Flying Light Specks
Trung Phan, Hamed Alimohammadzadeh, Heather Culbertson, Shahram Ghandeharizadeh
LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds
Zhenrong Zhang, Jianan Liu, Yuxuan Xia, Tao Huang, Qing-Long Han, Hongbin Liu
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds
Minhao Li, Zheng Qin, Zhirui Gao, Renjiao Yi, Chenyang Zhu, Yulan Guo, Kai Xu
SC3K: Self-supervised and Coherent 3D Keypoints Estimation from Rotated, Noisy, and Decimated Point Cloud Data
Mohammad Zohaib, Alessio Del Bue
Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration
Shaocong Liu, Tao Wang, Yan Zhang, Ruqin Zhou, Li Li, Chenguang Dai, Yongsheng Zhang, Longguang Wang, Hanyun Wang
GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity
Qi Yang, Joel Jung, Xiaozhong Xu, Shan Liu
Self-supervised Learning of Rotation-invariant 3D Point Set Features using Transformer and its Self-distillation
Takahiko Furuya, Zhoujie Chen, Ryutarou Ohbuchi, Zhenzhong Kuang
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
Yichao Shen, Zigang Geng, Yuhui Yuan, Yutong Lin, Ze Liu, Chunyu Wang, Han Hu, Nanning Zheng, Baining Guo
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Chensheng Peng, Guangming Wang, Xian Wan Lo, Xinrui Wu, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang