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
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
Yong He, Hongshan Yu, Muhammad Ibrahim, Xiaoyan Liu, Tongjia Chen, Anwaar Ulhaq, Ajmal Mian
Training point-based deep learning networks for forest segmentation with synthetic data
Francisco Raverta Capua, Juan Schandin, Pablo De Cristóforis
Surface Reconstruction from Point Clouds via Grid-based Intersection Prediction
Hui Tian, Kai Xu
HVDistill: Transferring Knowledge from Images to Point Clouds via Unsupervised Hybrid-View Distillation
Sha Zhang, Jiajun Deng, Lei Bai, Houqiang Li, Wanli Ouyang, Yanyong Zhang
Just Add $100 More: Augmenting NeRF-based Pseudo-LiDAR Point Cloud for Resolving Class-imbalance Problem
Mincheol Chang, Siyeong Lee, Jinkyu Kim, Namil Kim
ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds
Qijian Zhang, Junhui Hou, Ying He
RangeLDM: Fast Realistic LiDAR Point Cloud Generation
Qianjiang Hu, Zhimin Zhang, Wei Hu
Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment
Ziyu Shan, Yujie Zhang, Qi Yang, Haichen Yang, Yiling Xu, Jenq-Neng Hwang, Xiaozhong Xu, Shan Liu
Visual Foundation Models Boost Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Jingyi Xu, Weidong Yang, Lingdong Kong, Youquan Liu, Rui Zhang, Qingyuan Zhou, Ben Fei
PCLD: Point Cloud Layerwise Diffusion for Adversarial Purification
Mert Gulsen, Batuhan Cengiz, Yusuf H. Sahin, Gozde Unal
epsilon-Mesh Attack: A Surface-based Adversarial Point Cloud Attack for Facial Expression Recognition
Batuhan Cengiz, Mert Gulsen, Yusuf H. Sahin, Gozde Unal
Point Mamba: A Novel Point Cloud Backbone Based on State Space Model with Octree-Based Ordering Strategy
Jiuming Liu, Ruiji Yu, Yian Wang, Yu Zheng, Tianchen Deng, Weicai Ye, Hesheng Wang
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation
Peng Zhang, Ting Wu, Jinsheng Sun, Weiqing Li, Zhiyong Su