國立虎尾科技大學 |

Towards Optimal Point Cloud Processing for 3D Reconstruction

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Towards Optimal Point Cloud Processing for 3D Reconstruction/ by Guoxiang Zhang, YangQuan Chen.
作者:	Zhang, Guoxiang.
其他作者:	Chen, YangQuan.
面頁冊數:	XIX, 87 p. 29 illus., 25 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machines, Tools, Processes. -
電子資源:	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. - 1st ed. 2022. - XIX, 87 p. 29 illus., 25 illus. in color.online resource. - 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:

1366415
Machines, Tools, Processes.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Towards Optimal Point Cloud Processing for 3D Reconstruction
LDR:03126nam a22004215i 4500 001 1087040
003 DE-He213
005 20220603143540.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030961107 $9 978-3-030-96110-7
024 7 $a 10.1007/978-3-030-96110-7 $2 doi
035 $a 978-3-030-96110-7
050 4 $a TK5102.9
072 7 $a TJF $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TJF $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Zhang, Guoxiang. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1393981
245 1 0 $a Towards Optimal Point Cloud Processing for 3D Reconstruction $h [electronic resource] / $c by Guoxiang Zhang, YangQuan Chen.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XIX, 87 p. 29 illus., 25 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Signal Processing, $x 2196-4084
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.
650 2 4 $a Machines, Tools, Processes. $3 1366415
650 2 4 $a Geotechnical Engineering and Applied Earth Sciences. $3 1366420
650 2 4 $a Robotic Engineering. $3 1387599
650 2 4 $a Digital and Analog Signal Processing. $3 1366690
650 2 4 $a Machine Learning. $3 1137723
650 1 4 $a Signal, Speech and Image Processing . $3 1366353
650 0 $a Manufactures. $3 680602
650 0 $a Geotechnical engineering. $3 1024219
650 0 $a Robotics. $3 561941
650 0 $a Machine learning. $3 561253
650 0 $a Signal processing. $3 561459
700 1 $a Chen, YangQuan. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 888187
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030961091
776 0 8 $i Printed edition: $z 9783030961114
830 0 $a SpringerBriefs in Signal Processing, $x 2196-4076 $3 1257405
856 4 0 $u https://doi.org/10.1007/978-3-030-96110-7
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)