Draft:Object capture
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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]
See also
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 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.
- Technology
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