Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry

Three dimensional (3D) modelling of physical objects can be very useful in many areas, such as computer graphics and animation, robot vision, reverse engineering, and medical. 3D modelling can be done from the scratch using modelling software,or digitised from real world objects. The process of mo...

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Main Author:	Ng, Seng Beng
Format:	Thesis
Language:	English
Published:	2015
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Online Access:	http://psasir.upm.edu.my/id/eprint/65275/1/FSKTM%202015%2021IR.pdf
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spelling	my-upm-ir.652752018-09-03T08:14:16Z Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry 2015-01 Ng, Seng Beng Three dimensional (3D) modelling of physical objects can be very useful in many areas, such as computer graphics and animation, robot vision, reverse engineering, and medical. 3D modelling can be done from the scratch using modelling software,or digitised from real world objects. The process of modelling with software often consumes much time and requires a steep learning curve. On the other hand,conventional digitisation methods utilise Coordinate Measuring Machines (CMMs) or laser scanners. Nevertheless, both of these devices are very costly and require a certain amount of technical knowledge during usage and maintenance. An alternative approach which sacrifices some accuracy to greatly reduce the implementation costs will be Image-Based Modelling (IBM). This research introduces an IBM technique using optical flow and trigonometry with images captured via webcams. The implementation cost is reduced as it only requires a laptop, a webcam and a simple experiment setup. Image pairs with known small angle rotations and distance from the camera are the required inputs. Feature points were detected using good features to track and the displacement magnitudes were obtained via pyramidal implementation of Lucas Kanade Optical Flow. Optical flow magnitudes were then related with trigonometry to deduct the depth values of the feature points. The solution was able to combine feature points from all sides to produce a set of 3D surface points. Colour information of the feature points can be extracted as well. Data enhancement algorithms were implemented to perform noise filtering and inverse perspective mapping (IPM). Experiments were carried out with eight small complex shaped objects placed 300 mm away from the webcam. On average, the processing capacity for the solution was 1519 points per second. The average error on the approximated width dimension was 3.27% of the actual width while the average error on the depth dimension was 6.88% of the actual depth. The solution may work with as few as four images to generate a full set of 3D surface points. Future research may work on using the detected 3D point cloud as control points for texture coordinates to produce a fully texture mapped 3D model. 2015-01 Thesis http://psasir.upm.edu.my/id/eprint/65275/ http://psasir.upm.edu.my/id/eprint/65275/1/FSKTM%202015%2021IR.pdf text en public doctoral Universiti Putra Malaysia
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic
spellingShingle	Ng, Seng Beng Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
description	Three dimensional (3D) modelling of physical objects can be very useful in many areas, such as computer graphics and animation, robot vision, reverse engineering, and medical. 3D modelling can be done from the scratch using modelling software,or digitised from real world objects. The process of modelling with software often consumes much time and requires a steep learning curve. On the other hand,conventional digitisation methods utilise Coordinate Measuring Machines (CMMs) or laser scanners. Nevertheless, both of these devices are very costly and require a certain amount of technical knowledge during usage and maintenance. An alternative approach which sacrifices some accuracy to greatly reduce the implementation costs will be Image-Based Modelling (IBM). This research introduces an IBM technique using optical flow and trigonometry with images captured via webcams. The implementation cost is reduced as it only requires a laptop, a webcam and a simple experiment setup. Image pairs with known small angle rotations and distance from the camera are the required inputs. Feature points were detected using good features to track and the displacement magnitudes were obtained via pyramidal implementation of Lucas Kanade Optical Flow. Optical flow magnitudes were then related with trigonometry to deduct the depth values of the feature points. The solution was able to combine feature points from all sides to produce a set of 3D surface points. Colour information of the feature points can be extracted as well. Data enhancement algorithms were implemented to perform noise filtering and inverse perspective mapping (IPM). Experiments were carried out with eight small complex shaped objects placed 300 mm away from the webcam. On average, the processing capacity for the solution was 1519 points per second. The average error on the approximated width dimension was 3.27% of the actual width while the average error on the depth dimension was 6.88% of the actual depth. The solution may work with as few as four images to generate a full set of 3D surface points. Future research may work on using the detected 3D point cloud as control points for texture coordinates to produce a fully texture mapped 3D model.
format	Thesis
qualification_level	Doctorate
author	Ng, Seng Beng
author_facet	Ng, Seng Beng
author_sort	Ng, Seng Beng
title	Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
title_short	Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
title_full	Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
title_fullStr	Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
title_full_unstemmed	Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry
title_sort	depth value approximation of 2d complex-shape objects for 3d modelling using optical flow and trigonometry
granting_institution	Universiti Putra Malaysia
publishDate	2015
url	http://psasir.upm.edu.my/id/eprint/65275/1/FSKTM%202015%2021IR.pdf
_version_	1747812331134386176

Depth value approximation of 2D complex-shape objects for 3D modelling using optical flow and trigonometry

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