Point Cloud-Based 3D Reconstruction and Visualization

Dear Colleagues,

The rapid advancement of point cloud data processing technologies, coupled with the increasing availability of high-resolution 3D data, has significantly influenced various industries and research domains. These innovations have contributed to a paradigm shift in fields such as computer vision, robotics, and virtual reality, marking the beginning of a new era in 3D reconstruction and visualization.

One of the key trends in this area is the development of robust methods for point cloud-based 3D reconstruction. Researchers are focusing on overcoming challenges such as noise reduction, data sparsity, and real-time processing to enhance the accuracy and efficiency of 3D reconstruction systems. Notably, the integration of point clouds from multiple sensors and viewpoints is being studied to produce highly detailed and accurate 3D models in various applications, from autonomous vehicles to urban mapping and medical imaging.

Additionally, the visualization of 3D point cloud data is playing a critical role in transforming raw data into meaningful insights. Novel approaches in data rendering, interactive visualization, and real-time feedback are being developed to improve the interpretation and usability of 3D data. These advancements are leading to significant improvements in industries ranging from entertainment to industrial design and heritage preservation.

This Special Issue invites high-quality research papers that focus on point cloud-based 3D reconstruction and visualization. Submissions are expected to address key challenges and present cutting-edge solutions that will shape the future of 3D technologies across various domains.

Topics of interest include, but are not limited to, the following:

• Algorithms for point cloud segmentation and clustering;
• Multisensor data fusion for point cloud enhancement;
• Noise reduction and outlier detection in point cloud data;
• Real-time 3D reconstruction from point clouds;
• Machine learning and AI for point cloud-based object detection and classification;
• Visualization techniques for large-scale 3D point cloud data;
• Multimodal image fusion in 3D reconstruction;
• Applications of point cloud 3D reconstruction in autonomous systems;
• Point cloud registration and alignment methods;
• Deep learning for point cloud denoising and super-resolution.

Dr. Zhaoxiang Zhang
Prof. Dr. Yuelei Xu
Guest Editors

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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 segmentation
• point cloud data
• 3D reconstruction
• visualization