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
FFPA-Net: Efficient Feature Fusion with Projection Awareness for 3D Object Detection
Chaokang Jiang, Guangming Wang, Jinxing Wu, Yanzi Miao, Hesheng Wang
AssembleRL: Learning to Assemble Furniture from Their Point Clouds
Özgür Aslan, Burak Bolat, Batuhan Bal, Tuğba Tümer, Erol Şahin, Sinan Kalkan
4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds
Alvari Seppänen, Risto Ojala, Kari Tammi
Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking
Pan Wang, Liangliang Ren, Shengkai Wu, Jinrong Yang, En Yu, Hangcheng Yu, Xiaoping Li
MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment
Zicheng Zhang, Wei Sun, Xiongkuo Min, Quan Zhou, Jun He, Qiyuan Wang, Guangtao Zhai