Draft:Object capture

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Declined by Pythoncoder 5 months ago. Last edited by Pythoncoder 5 months ago. Reviewer: Inform author.

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Object capture is a workflow for reconstructing three-dimensional (3D) digital models from multiple overlapping photographs of a physical object.^[1]^[2] Typical pipelines combine camera pose estimation (often structure-from-motion), dense reconstruction, mesh generation, and texture mapping.^[3]^[4]

Object-capture methods are used in cultural-heritage documentation, visual-effects production, product digitization, and downstream 3D-print workflows.^[5]^[6]

Workflow

A common object-capture workflow includes:^[3]^[4]

Capture of overlapping images from multiple viewpoints.
Camera pose estimation and sparse scene reconstruction.
Dense reconstruction into depth maps or point clouds.
Surface reconstruction into a polygon mesh.
Texture generation and optional post-processing (for example, hole filling or mesh simplification).

Applications

Cultural heritage

Object capture is used to document artifacts, sculptures, and archaeological objects for preservation, measurement, and digital access.^[6]^[5]

Media and interactive content

Film and game pipelines use image-based reconstruction to create realistic environment and prop assets from physical scenes and objects.^[2]

3D printing

Captured meshes are often exported to manufacturing formats (such as OBJ or STL), repaired where needed, and prepared in slicer software before additive manufacturing.^[7]

Limitations

Accuracy can degrade on reflective or transparent surfaces, low-texture objects, or image sets with insufficient overlap and inconsistent lighting.^[8]

References

^ Hartley, Richard; Zisserman, Andrew (2004). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press. ISBN 978-0-521-54051-3.
^ ^a ^b Snavely, Noah; Seitz, Steven M.; Szeliski, Richard (2006). "Photo Tourism: Exploring Photo Collections in 3D". ACM Transactions on Graphics. 25 (3): 835–846. doi:10.1145/1141911.1141964.
^ ^a ^b Schönberger, Johannes L.; Frahm, Jan-Michael (2016). "Structure-from-Motion Revisited". 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4104–4113. doi:10.1109/CVPR.2016.445.
^ ^a ^b Furukawa, Yasutaka; Ponce, Jean (2010). "Accurate, Dense, and Robust Multiview Stereopsis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (8): 1362–1376. doi:10.1109/TPAMI.2009.161.
^ ^a ^b Remondino, Fabio; Del Pizzo, Salvatore; Kersten, Thomas P.; Troisi, Salvatore (2012). "Low-Cost and Open-Source Solutions for Automated Image Orientation—A Critical Overview". Lecture Notes in Computer Science. 7616: 40–54. doi:10.1007/978-3-642-34234-9_5.
^ ^a ^b Bruno, Fabio; Bruno, Stefania; De Sensi, Giuseppe; Luchi, Maria-Letizia; Mancuso, Stefano; Muzzupappa, Maurizio (2010). "From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition". Journal of Cultural Heritage. 11 (1): 42–49. doi:10.1016/j.culher.2009.02.006.
^ Gibson, Ian; Rosen, David W.; Stucker, Brent (2015). Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing (2nd ed.). Springer. doi:10.1007/978-1-4939-2113-3. ISBN 978-1-4939-2112-6.
^ Stutz, David; Hermans, Alexander; Leibe, Bastian (2018). "A Survey of RGB-D Scene Reconstruction". Foundations and Trends in Computer Graphics and Vision. 13 (1–2): 1–234. doi:10.1561/0600000071.

[hz2004-1] Hartley, Richard; Zisserman, Andrew (2004). Multiple View Geometry in Computer Vision (2nd ed.). Cambridge University Press. ISBN 978-0-521-54051-3.

[snavely2006-2] Snavely, Noah; Seitz, Steven M.; Szeliski, Richard (2006). "Photo Tourism: Exploring Photo Collections in 3D". ACM Transactions on Graphics. 25 (3): 835–846. doi:10.1145/1141911.1141964.

[sfm2016-3] Schönberger, Johannes L.; Frahm, Jan-Michael (2016). "Structure-from-Motion Revisited". 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4104–4113. doi:10.1109/CVPR.2016.445.

[mvs2010-4] Furukawa, Yasutaka; Ponce, Jean (2010). "Accurate, Dense, and Robust Multiview Stereopsis". IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 (8): 1362–1376. doi:10.1109/TPAMI.2009.161.

[remondino2011-5] Remondino, Fabio; Del Pizzo, Salvatore; Kersten, Thomas P.; Troisi, Salvatore (2012). "Low-Cost and Open-Source Solutions for Automated Image Orientation—A Critical Overview". Lecture Notes in Computer Science. 7616: 40–54. doi:10.1007/978-3-642-34234-9_5.

[bruno2010-6] Bruno, Fabio; Bruno, Stefania; De Sensi, Giuseppe; Luchi, Maria-Letizia; Mancuso, Stefano; Muzzupappa, Maurizio (2010). "From 3D reconstruction to virtual reality: A complete methodology for digital archaeological exhibition". Journal of Cultural Heritage. 11 (1): 42–49. doi:10.1016/j.culher.2009.02.006.

[gibson2015-7] Gibson, Ian; Rosen, David W.; Stucker, Brent (2015). Additive Manufacturing Technologies: 3D Printing, Rapid Prototyping, and Direct Digital Manufacturing (2nd ed.). Springer. doi:10.1007/978-1-4939-2113-3. ISBN 978-1-4939-2112-6.

[stutz2018-8] Stutz, David; Hermans, Alexander; Leibe, Bastian (2018). "A Survey of RGB-D Scene Reconstruction". Foundations and Trends in Computer Graphics and Vision. 13 (1–2): 1–234. doi:10.1561/0600000071.

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