Development of Photogrammetry Application for 3D Surface Reconstruction

Wendy Cahya Kurniawan,Fauzy Satrio Wibowo,Hsien-I Lin,Anik Nur Handayani,Siti Sendari

2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE)（2021）

Natl Taipei Univ Technol | Univ Negeri Malang

Cited 0|Views10

Abstract

Photogrammetry has long been used to collect three- dimensional (3D) information about objects or texture data. The 3D reproduction of real-life objects can be highly useful in a variety of fields, such as archeology, medicine, and geology. The advantages of photogrammetry research with lasers reduce costs, time to digitize objects, and have high accuracy and efficiency. This research aims to design an application that processes 2D image input data into 3D. The features contained in this application include data collection, calibration, image rectification, feature detection, and scanned images. The development of this application has succeeded in reconstructing 2D images into 3D images. In the test, the average visible point detection point is 185.4 and the mean reprojection error is 0.7542. The minimum calibration error must be obtained so that the surface reconstruction results are close to the actual shape. In the future, we would extend our work by manipulating robot movement with photogrammetry as input data.

Translated text

Key words

photogrammetry,3D model,3D image reconstruction,laser scanner

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper