000			04007nam a22006495i 4500
999			_c200457272 _d75484
003			TR-AnTOB
005			20231116163745.0
007			cr nn 008mamaa
008			220603s2022 sz | s |||| 0|eng d
020			_a9783030961107
024	7		_a10.1007/978-3-030-96110-7 _2doi
040			_aTR-AnTOB _beng _cTR-AnTOB _erda
041			_aeng
050		4	_aTK5102.9
072		7	_aTJF _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aTJF _2thema
072		7	_aUYS _2thema
090			_aTK5102.9EBK
100	1		_aZhang, Guoxiang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aTowards Optimal Point Cloud Processing for 3D Reconstruction _h[electronic resource] / _cby Guoxiang Zhang, YangQuan Chen.
250			_a1st ed. 2022.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Signal Processing, _x2196-4084
505	0		_a1. 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			_aThis 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		0	_aSignal processing.
650		0	_aMachine learning.
650		0	_aRobotics.
650		0	_aGeotechnical engineering.
650		0	_aManufactures.
650	1	4	_aSignal, Speech and Image Processing .
650	2	4	_aMachine Learning.
650	2	4	_aDigital and Analog Signal Processing.
650	2	4	_aRobotic Engineering.
650	2	4	_aGeotechnical Engineering and Applied Earth Sciences.
650	2	4	_aMachines, Tools, Processes.
653		0	_aSignal processing -- Mathematical models
653		0	_aSignal detection
653		0	_aCloud computing
653		0	_aThree-dimensional imaging
700	1		_aChen, YangQuan. _eauthor. _0(orcid)0000-0002-7422-5988 _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
830		0	_aSpringerBriefs in Signal Processing, _x2196-4084
856	4	0	_uhttps://doi.org/10.1007/978-3-030-96110-7 _3Springer eBooks _zOnline access link to the resource
942			_2lcc _cEBK