Dear Colleagues,

Over the past few decades, point clouds from reality capture devices have been major data sources used by the geo-information and remote sensing communities. Recently, processing massive geospatial point cloud data has drawn extensive attention from the robotics, computer vision and computer graphics communities. The drive of this call is primarily to grow interdisciplinary interaction, interoperable frameworks and collaboration in point cloud processing among geo-information, photogrammetry, remote sensing, computer vision, computer graphics and robotics.

Applications are numerous, and potentially increasing if we consider point clouds as digital reality assets. Yet, this expansion faces technical limitations mainly from the lack of semantic information within point ensembles. Connecting knowledge sources is still a very manual and time-consuming process which can suffer from error-prone human interpretation. This highlights a strong need for automated solutions to create coherent and structured information that can use relevant and flexible feature descriptors. This theme issue aims at gathering new insights in point cloud processing to better characterize these spatial datasets for various tasks while strongly considering interoperable usages. Specifically, application-driven processes linked to spatial information modelling (CAD, BIM, GIS), infrastructure management, cultural heritage and remote sensing applications are encouraged.

We anticipate that the advancement of data mining and the infatuation of reality capture will continue to push the research communities forward. Particularly, ways to obtain high-quality application-oriented labelled datasets will permit a wider dissemination of robust learning approaches. Henceforth, we encourage authors to submit original research articles, review papers and case studies from both theoretical and application-oriented perspectives on this significant and exciting subject. In more details, topics suitable for this Special Issue include (but are not necessarily limited to):

• Georeferenced point clouds from laser scanners (mobile, hand-held, backpack-mounted, terrestrial, aerial)
• Point clouds from panoramas, phone/cameras images, oblique and satellite imagery
• Point Cloud segmentation, classification, semantic enrichment for application-driven scenario
• Point Cloud structuration and knowledge integration
• Point Cloud Knowledge extraction and high-performance feature extraction for large-scale datasets
• 2D floorplan generation of indoor point clouds
• Industrial applications with large-scale point clouds
• Feature-based rendering and visualization of large-scale point clouds
• Deep learning for point cloud processing

Dr. Florent Poux
Dr. Roland Billen
Guest Editors

Manuscript Submission Information

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• Feature selection, feature recognition, feature extraction and feature fusion
• Semantic segmentation, instance segmentation and object recognition
• Feature-based classification, learning-based classification, deep learning
• 3D Point Cloud, terrestrial laser scanner, LiDAR, photogrammetry
• 3D data structure, knowledge integration, knowledge representation, ontology
• Infrastructure, remote sensing, cultural heritage, BIM, CAD, GIS