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Book synopsis

This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods.The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data.

Book details

Book category Books in English Technology, engineering, agriculture Other technologies & applied sciences Applied optics

• Full title: Towards Optimal Point Cloud Processing for 3D Reconstruction
• Author: Guoxiang Zhang, YangQuan Chen
• Language: English
• Binding: Paperback
• Number of pages: 87
• EAN: 9783030961091
• ID: 38505219
• Publisher: Springer Nature Switzerland AG
• Weight: 180 g
• Dimensions: 235 × 155 × 7 mm
• Date of publishing: 04. June 2022

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