Cuboid (computer vision)

Short description: Basic 3D shape extracted from a 2D image

In computer vision, the term cuboid is used to describe a small spatiotemporal volume extracted for purposes of behavior recognition.[1] The cuboid is regarded as a basic geometric primitive type and is used to depict three-dimensional objects within a three dimensional representation of a flat, two dimensional image.[2]

## Production

Cuboids can be produced from both two-dimensional and three-dimensional images.[3]

One method used to produce cuboids utilizes scene understanding (SUN) primitive databases, which are collections of pictures that already contain cuboids. By sorting through SUN primitive databases with machine learning tools, computers observe the conditions in which cuboids are produced in images from SUN primitive databases and can learn to produce cuboids from other images.[2]

RGB-D images, which are RGB images that also record the depth of each pixel, are occasionally used to produce cuboids because computers no longer need to determine the depth of an object, as they typically do because depth is already recorded.[4]

Cuboid production is sensitive to changes in color and illumination, blockage, and background clutter. This means that it is difficult for computers to produce cuboids of objects that are multicolored, irregularly illuminated, or partially covered, or if there are many objects in the background. This is partially due to the fact that algorithms for producing cuboids are still relatively simple.[3]

## Usage

Cuboids are created for point cloud-based three-dimensional maps and can be utilized in various situations such as augmented reality,[5] the automated control of cars, drones, and robots,[4] and object detection.[3]

Cuboids allow for software to identify a scene through geometric descriptions in an “object-agnostic” fashion.[2]

Interest points, locations within images that are identified by a computer as essential to identifying the image, created from two-dimensional images can be used with cuboids for image matching, identifying a room or scene, and instance recognition. Interest points created from three dimensional images can be used with cuboids to recognize activities. This is possible because interest points aid software to focus on only the most important aspects of the images.[3]

RGB-D images and SLAM systems are used together in RGB-D SLAM systems, which are employed by Computer-aided design systems to generate point cloud-based three-dimensional maps.[4]

Most industrial multi-axis machining tools use computer-aided manufacturing and subsequently work in cuboid work spaces.[6]

## References

1. ↑ P. Dollár; V. Rabaud; G. Cottrell; S. Belongie (October 2005). "Behavior Recognition via Sparse Spatio-Temporal Features". 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance. pp. 65–72. doi:10.1109/VSPETS.2005.1570899.
2. ↑ 2.0 2.1 2.2 Xiao, Jianxiong; Russell, Bryan C.; Torralba, Antonio (2012). "Localizing 3D Cuboids in Single-view Images". Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. NIPS'12 (USA: Curran Associates Inc.): 746–754. http://dl.acm.org/citation.cfm?id=2999134.2999218.
3. ↑ 3.0 3.1 3.2 3.3 Aggarwal, J. K.; Xia, Lu (2013). Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera. pp. 2834–2841. https://www.cv-foundation.org/openaccess/content_cvpr_2013/html/Xia_Spatio-temporal_Depth_Cuboid_2013_CVPR_paper.html.
4. ↑ 4.0 4.1 4.2 Mishima, Masashi; Uchiyama, Hideaki; Thomas, Diego; Taniguchi, Rin-ichiro; Roberto, Rafael; Lima, João Paulo; Teichrieb, Veronica (2019-01-06). "Incremental 3D Cuboid Modeling with Drift Compensation". Sensors (Basel, Switzerland) 19 (1): 178. doi:10.3390/s19010178. ISSN 1424-8220. PMID 30621340. Bibcode: 2019Senso..19..178M.
5. ↑ and, and (September 1999). "New calibration-free approach for augmented reality based on parameterized cuboid structure". Proceedings of the Seventh IEEE International Conference on Computer Vision. 1. pp. 30–37 vol.1. doi:10.1109/ICCV.1999.791194. ISBN 0-7695-0164-8.
6. ↑ Wang, Z.; Wang, G.; Ji, S.; Wan, Y.; Yuan, Q. (December 2007). "Optimal design of a linear Delta robot for the prescribed cuboid dexterous workspace". 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO). pp. 2183–2188. doi:10.1109/ROBIO.2007.4522508. ISBN 978-1-4244-1761-2.

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