Advances in Understanding and 3D Semantic Modeling of Large-Scale Urban Scenes from Point Clouds (Second Edition)

Dear Colleagues,

Following the success of our previous Special Issue “Advances in Understanding and 3D Semantic Modeling of Large-Scale Urban Scenes from Point Clouds”, we are happy to announce a new one has been created.

Driven by many applications and the improvement of 3D data acquisition technology, the computer vision and remote sensing communities are now focusing on deep learning-based and knowledge-based algorithms to tackle the challenges in understanding the 3D semantic modeling of large-scale urban scenes. The physical modeling of scenes from point clouds includes the enhancement and segmentation of the scene at the object and part level, shape recognition, indoor and outdoor abstraction, reconstruction, and an optional simplification to make the 3D model web and/or mobile compatible. Although recent advanced deep-learning algorithms have exhibited powerful performance when carrying out low-level recognition tasks such as classification and segmentation, scant attention has been given to deep learning for large-scale 3D urban modeling due to a lack of available training data or benchmark repositories. Other challenges include detail modeling from imperfect (occluded or noisy) scans, free-form building modeling, lightweight modeling for web/mobile compatibility, flexible modeling which generates multiple levels of detail (LoDs) on the fly, and automated reconstruction from large-scale urban point clouds, to name a few. We position our Special Issue to support the ongoing efforts in the 3D scanning and modeling industry by focusing on applications of LiDAR/RGB-D/photogrammetric point clouds. The topics addressed within this Special Issue may encompass a wide array of subjects, including but not limited to:

• The enhancement, registration, and filtering of point clouds;
• Semantic, instance, panoptic, and part-level segmentation;
• Large-scale outdoor scene and indoor scene reconstruction;
• Detail synthesis and implicit modeling of urban scenes;
• The 3D modeling of buildings, bridges, roads, trees, and utilities;
• The rendering and visualization of urban scenes;
• Polyhedral meshes, procedural models, and model simplification;
• Deep learning-based reconstruction and point-based neural radiance fields;
• Innovative applications in smart cities, VR/AR, autonomous driving, indoor navigation, etc.

Dr. Dong Chen
Dr. Jiaming Na
Dr. Jiju Poovvancheri
Prof. Dr. Norbert Pfeifer
Guest Editors

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