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
CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction
Wenhao Lan, Yijun Yang, Haihua Shen, Shan Li
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Rui She, Qiyu Kang, Sijie Wang, Wee Peng Tay, Kai Zhao, Yang Song, Tianyu Geng, Yi Xu, Diego Navarro Navarro, Andreas Hartmannsgruber
On Support Relations Inference and Scene Hierarchy Graph Construction from Point Cloud in Clustered Environments
Gang Ma, Hui Wei
A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning
Yu-Xin Zhang, Jie Gui, Xiaofeng Cong, Xin Gong, Wenbing Tao
A Hybrid Generative and Discriminative PointNet on Unordered Point Sets
Yang Ye, Shihao Ji
Weakly Supervised LiDAR Semantic Segmentation via Scatter Image Annotation
Yilong Chen, Zongyi Xu, xiaoshui Huang, Ruicheng Zhang, Xinqi Jiang, Xinbo Gao
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada
MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model
Kang Zeng, Hao Shi, Jiacheng Lin, Siyu Li, Jintao Cheng, Kaiwei Wang, Zhiyong Li, Kailun Yang
VoxAtnNet: A 3D Point Clouds Convolutional Neural Network for Generalizable Face Presentation Attack Detection
Raghavendra Ramachandra, Narayan Vetrekar, Sushma Venkatesh, Savita Nageshker, Jag Mohan Singh, R. S. Gad
Evaluating Alternatives to SFM Point Cloud Initialization for Gaussian Splatting
Yalda Foroutan, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi
Spot-Compose: A Framework for Open-Vocabulary Object Retrieval and Drawer Manipulation in Point Clouds
Oliver Lemke, Zuria Bauer, René Zurbrügg, Marc Pollefeys, Francis Engelmann, Hermann Blum
Point-In-Context: Understanding Point Cloud via In-Context Learning
Mengyuan Liu, Zhongbin Fang, Xia Li, Joachim M. Buhmann, Xiangtai Li, Chen Change Loy