Authors: Prof. Jitendra Gaikwad, Aditya Patil, Ritiksha Pardhi, Bhavesh Patil, Aaryen Patil
DOI Link: https://doi.org/10.22214/ijraset.2024.62806
Certificate: View Certificate
In today\'s dynamic market landscape, the demand for precise 3D reconstruction methods is soaring across industries like architecture, entertainment, and manufacturing. Object scanning technologies offer a pivotal solution, providing detailed digital representations of real-world objects. With benefits ranging from accelerated product development cycles to enhanced cultural heritage preservation and the compact design of scanning systems, these advancements promise to revolutionize workflows, streamline processes, and drive innovation in an array of sectors, all while offering the added benefit of portability for on-the-go applications.
I. INTRODUCTION
The process of 3D reconstruction plays a crucial role in various domains, including manufacturing, cultural heritage preservation, virtual reality, and augmented reality.
Within this realm, object scanning serves as a critical component, allowing for the capture of intricate details and spatial information essential for an accurate representation of the physical world. This paper addresses the evolving landscape of object scanning techniques and their pivotal role in the overarching goal of creating detailed 3D models.
In recent years, there has been a paradigm shift in the methodologies employed for object scanning, driven by advancements in sensor technologies, computer vision algorithms, and computational capabilities. Traditional approaches often faced challenges in capturing fine details, handling complex surfaces, and achieving real-time performance. The emergence of novel scanning technologies, such as structured light, laser, and depth sensing, has revolutionized the landscape, offering solutions to overcome these challenges and pushing the boundaries of what is achievable in 3D reconstruction.
This paper's main goal is to provide an extensive overview of the various object scanning techniques currently employed in 3D reconstruction applications. From contact-based methods to non-contact technologies, the survey encompasses a broad spectrum of approaches, highlighting their strengths, limitations, and real-world applications. The review also delves into the integration of machine learning and artificial intelligence in enhancing the accuracy and efficiency of object scanning processes, paving the way for autonomous and adaptive systems.
Delving into the intricacies of object scanning for 3D reconstruction, it becomes evident that the field is dynamic and continually evolving. Researchers and practitioners are confronted with the challenge of selecting the most suitable technique for specific applications, considering factors such as accuracy, speed, cost, and scalability. This paper aims to serve as a valuable resource for those navigating the diverse landscape of object scanning, providing insights into the latest developments and guiding future research directions in the pursuit of more refined and versatile 3D reconstruction methodologies.
II. LITERATURE SURVEY
III. METHODOLOGY/EXPERIMENTAL
As shown in the fig 1, the various blocks in the system are
A. Object Scanning Setup:
The object scanning process initiates with the configuration of a controlled environment, ensuring optimal conditions for accurate data capture. The setup involves the placement of the target object within the scanning area (from 2- 15cm), with consideration given to lighting conditions and background control.
B. Data Acquisition
There is a SHARP IR sensor that continuously detects and measures the coordinates of the object which is continuously rotating with the help of a turntable motor. It provides every coordinate of the object layer by layer and helps in creating the mesh form of the object.
C. Calculations
The turntable motor is at a distance of 8 cm from the IR sensor. The z axis goes up by 1cm for every 8 rotations of the z axis motor. The Z axis motor will keep rotation till it reaches its maximum height. For every instance, 40 values of distance are noted and are averaged. This distance is the n mapped from 0 to 1024 and 0 to3.3 V
The final distance is calculated as
D= -5.40274 * Davg3 + 28.4823 * Davg2 - 49.7115(Davg) + 31.3444
(all these values were taken from the datasheet for the IR sensor)
The average is then used to find out the final values of x and y using the angle of the motor using
x = sin(angle) x D, and
y = cos(angle) * D
D. Point Cloud Generation
A point cloud, which represents a collection of data points in a three-dimensional coordinate system, is created by processing the acquired data. The cornerstone for the ensuing model rebuilding is this fundamental phase.
E. Mesh Generation and Texturing
The point cloud is utilized to create a mesh, defining the object's surface geometry. Texture information is applied to enhance visual realism, resulting in a complete 3D model.
F. Post-Processing and Refinement
The final step involves post-processing techniques to refine the 3D model, addressing noise, outliers, and any artifacts introduced during the scanning process. Iterative refinement ensures a high-fidelity representation of the scanned object.
G. Working of the system
The proposed 3D scanning system integrates two stepper motors, an Arduino UNO microcontroller, an IR sensor, and a micro-SD card reader module to facilitate the digitization of physical objects. The coordinated movement of a rotating platform and a vertically traversing platform, both driven by calibrated stepper motors, enables precise scanning. The system operates in conjunction with a toggle switch for seamless platform movement and is powered by an external power supply.
In conclusion, the presented 3D scanning system provides a valuable tool for capturing detailed digital models of physical objects. While acknowledging limitations such as scan volume constraints and surface considerations, the system\'s automated approach, synchronized stepper motors, and versatile applications underscore its significance. Future developments hold the potential to refine precision, expand capabilities, and address current constraints, positioning this technology as a promising asset for industries ranging from manufacturing to digital preservation.
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Copyright © 2024 Prof. Jitendra Gaikwad, Aditya Patil, Ritiksha Pardhi, Bhavesh Patil, Aaryen Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET62806
Publish Date : 2024-05-27
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here