Special Issue "Recent Trends in 3D Modelling from Point Clouds"
Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 3375
Interests: LiDAR; 3D scene perception and analysis; Environmental remote sensing; Sensor fusion
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Point clouds are deemed to be one of the foundational pillars in representing the 3D digital world, despite their irregular topology among the discrete points associated with them. Recently, advancements in sensor technologies that acquire point cloud data for a flexible and scalable geometric representation have paved the way for the development of new ideas, methodologies, and solutions in countless remote sensing applications. These state-of-the-art sensors can capture and describe objects in a scene using dense point clouds from various platforms (satellite, aerial, UAV, vehicle-borne, backpack, handheld, and static terrestrial), perspectives (nadir, oblique, and side-view), spectra (multispectral), and granularity (point density and completeness). In the last two decades, point clouds generated from images or directly acquired from Lidar (including ALS, MLS, BLS, and TLS) have become the main source for 3D modeling. Many algorithms have since been made available in the form of data-driven, model-driven or hybrid approaches to reconstruct 3D models with semantic information. The latest techniques in deep learning have even made it possible to predict 3D models from point clouds.
The Special Issue aims at contributions that focus on processing and utilizing point cloud data acquired from laser scanners and other 3D imaging systems. We are particularly interested in original papers that address innovative techniques for generating, handling, and analyzing point cloud data, challenges in dealing with point cloud data in emerging remote sensing applications, and which develop new applications for point cloud data. Additionally, we look forward to seeing new algorithms, techniques, and applications of generating 3D city models or digital twins from point cloud data.
Prof. Dr. Wei Yao
Prof. Dr. Hongchao Fan
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Point cloud acquisition from the laser scanner, stereo vision, panoramas, camera phone images, oblique and satellite imagery
- Deep learning for point cloud processing
- Point cloud registration and segmentation
- Feature extraction, object detection, semantic labeling, and change detection
- Point cloud processing for indoor modeling and BIM
- Digital twins from point clouds
- Semantic city models from 3D point clouds
- Fusion of multimodal point clouds with imagery for object classification and modeling
- Modeling urban and natural environment from aerial and mobile LiDAR/image-based point clouds
- Industrial applications with large-scale point clouds
- High-performance computing for large-scale point clouds