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
Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching
Matteo Bastico, Etienne Decencière, Laurent Corté, Yannick Tillier, David Ryckelynck
CAPT: Category-level Articulation Estimation from a Single Point Cloud Using Transformer
Lian Fu, Ryoichi Ishikawa, Yoshihiro Sato, Takeshi Oishi
Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships
Sebastian Koch, Narunas Vaskevicius, Mirco Colosi, Pedro Hermosilla, Timo Ropinski
Real-time 3D Semantic Scene Perception for Egocentric Robots with Binocular Vision
K. Nguyen, T. Dang, M. Huber
Transferring facade labels between point clouds with semantic octrees while considering change detection
Sophia Schwarz, Tanja Pilz, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
Reconstructing facade details using MLS point clouds and Bag-of-Words approach
Thomas Froech, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
Classifying point clouds at the facade-level using geometric features and deep learning networks
Yue Tan, Olaf Wysocki, Ludwig Hoegner, Uwe Stilla
MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
Olaf Wysocki, Ludwig Hoegner, Uwe Stilla