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Towards optimal point cloud processi...

Zhang, Guoxiang.

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Towards optimal point cloud processing for 3D reconstruction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards optimal point cloud processing for 3D reconstruction/ by Guoxiang Zhang, YangQuan Chen.
Author:	Zhang, Guoxiang.
other author:	Chen, YangQuan.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xix, 87 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Preliminaries -- 3. Fractional-Order Random Sample Consensus -- 4. Online Sifting of Loop Detections for 3D Reconstruction of Caves -- 5. Dense Map Posterior: A Novel Quality Metric for 3D Reconstruction -- 6. Offline Sifting and Majorization of Loop Detections -- 7. Conclusion and Future Opportunities -- Appendix: More Information on Results Reproducibility.
Contained By:	Springer Nature eBook
Subject:	Signal processing - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-3-030-96110-7
ISBN:	9783030961107

Towards optimal point cloud processing for 3D reconstruction
Zhang, Guoxiang.

Towards optimal point cloud processing for 3D reconstruction[electronic resource] /by Guoxiang Zhang, YangQuan Chen. - Cham :Springer International Publishing :2022. - xix, 87 p. :ill. (chiefly col.), digital ;24 cm. - SpringerBriefs in signal processing,2196-4084. - SpringerBriefs in signal processing..

1. Introduction -- 2. Preliminaries -- 3. Fractional-Order Random Sample Consensus -- 4. Online Sifting of Loop Detections for 3D Reconstruction of Caves -- 5. Dense Map Posterior: A Novel Quality Metric for 3D Reconstruction -- 6. Offline Sifting and Majorization of Loop Detections -- 7. Conclusion and Future Opportunities -- Appendix: More Information on Results Reproducibility.

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. The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.

ISBN: 9783030961107

Standard No.: 10.1007/978-3-030-96110-7doiSubjects--Topical Terms:

678506
Signal processing
--Mathematical models.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.3822

Towards optimal point cloud processing for 3D reconstruction
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505 0 $a 1. Introduction -- 2. Preliminaries -- 3. Fractional-Order Random Sample Consensus -- 4. Online Sifting of Loop Detections for 3D Reconstruction of Caves -- 5. Dense Map Posterior: A Novel Quality Metric for 3D Reconstruction -- 6. Offline Sifting and Majorization of Loop Detections -- 7. Conclusion and Future Opportunities -- Appendix: More Information on Results Reproducibility.
520 $a 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. The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.
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