Towards Optimal Point Cloud Processing for 3D Reconstruction

Name: Towards Optimal Point Cloud Processing for 3D Reconstruction
Brand: Springer Nature Switzerland AG
SKU: 9783030961091
Price: 1260 CZK
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Autor: Guoxiang Zhang, YangQuan Chen

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer Nature Switzerland AG

Dostupnost: Skladem u dodavatele

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This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction...

Informace o knize

Autor

Guoxiang Zhang, YangQuan Chen

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2022

Stránek

EAN

9783030961091

Enbook ID

38505219

Vydavatel

Springer Nature Switzerland AG

Hmotnost

180

Rozměry

155 x 235 x 7

Kompletní popis

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.

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Towards Optimal Point Cloud Processing for 3D Reconstruction

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