Abstract
The increase in computational power in recent years has opened a new door for image processing techniques. Three-dimensional object recognition, identification, pose estimation, and mapping are becoming popular. The need for real-world objects to be mapped into three-dimensional spatial representation is greatly increasing, especially considering the heap jump we obtained in the past decade in virtual reality and augmented reality. This paper discusses an algorithm to convert an array of captured images into estimated 3D coordinates of their external mappings. Elementary methods for generating three-dimensional models are also discussed. This framework will help the community in estimating three-dimensional coordinates of a convex-shaped object from a series of two-dimension images. The built model could be further processed for increasing the resemblance of the input object in terms of its shapes, contour, and texture.
Highlights
A designer has to spend a considerable amount of time designing each component with respect to its depth, shape, and other characteristics in computer-aided design (CAD) software. e design process is tiring and requires skilled labour as each component with respect to its depth, shape, and other characteristics that have to be designed [12, 13]
There are various studies that discuss different algorithms for generating three-dimensional maps for stereoscopic images, SAR images, and so on. e recent digitalization insists on progressing beyond the limitations of traditional photo processing techniques. ere are studies that compare existing reconstruction algorithms used in a variety of applications. e survey focusing on triangulation and stereo-vision [16,17,18,19] compared the speed, accuracy, and practicality of different algorithms used in the motionparallax scenario. is study enumerates different approaches such as image-based, voxel-based, and objectbased approaches for scene and geometric parallax reconstruction [20, 21]
Even though there was no mention of generation of object coordinates, the bounding box method was used on objects [22] for its pose estimation
Summary
A designer has to spend a considerable amount of time designing each component with respect to its depth, shape, and other characteristics in computer-aided design (CAD) software. e design process is tiring and requires skilled labour as each component with respect to its depth, shape, and other characteristics that have to be designed [12, 13].e use of three-dimensional models in applications is increasingly becoming popular. ere is a significant trade off between the quality of generated models and the manual effort applied in the process of modelling. Is study consists of suggesting methods to convert an object into its three-dimensional mapping. There are various studies that discuss different algorithms for generating three-dimensional maps for stereoscopic images, SAR images, and so on. Ere are studies that compare existing reconstruction algorithms used in a variety of applications. SV3DVision [23] is a depth-map generating algorithm used for reconstruction of scenes based on a single-photograph input. E above proposed method uses a singlephotograph input whereas a more calibrated method [24] using parallel axes uses a stereoscopic system Both these studies focus on identification of near-placed and far-placed objects for depth-map generation, focusing on robotic vision applications [25]. A silhouette-based method [26, 27] based on volume intersection approach is available in the 3D model reconstruction research area. e above proposed study uses camera calibration and bounding cube estimation for silhouette extraction using triangulation and decimation
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