KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD
Point cloud data is type of data that used as raw data for 3D modelling of real object like buildings This happen because the development of survey acquisition technology such as active sensors like laser scanner and the development of image matching algorithm in photogrammetry that can produce poin...
Saved in:
Main Author: | |
---|---|
Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/32187 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
id |
id-itb.:32187 |
---|---|
spelling |
id-itb.:321872018-12-04T15:29:40ZKAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD Arthur Nabiel, Torang Indonesia Final Project 3Don model, point cloud, deteksi bentuk, Random Sample Consesnsus (RANSAC), primitives geometry form. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/32187 Point cloud data is type of data that used as raw data for 3D modelling of real object like buildings This happen because the development of survey acquisition technology such as active sensors like laser scanner and the development of image matching algorithm in photogrammetry that can produce point cloud data. Those point cloud data has position information in three dimensional coordinates. To produce 3D model, point cloud data has several steps to be done such as classification, deteksi bentuk and reconstruction. Every object in real world is contructed by primitives geometry form such as planes, cylinder, cone, etc. The outcome that expected from deteksi bentuk process is this process can generate simplified model from point cloud data. Futhermore those model must also covered all the point cloud data and mantain it geometry as the original object. This process also can be used for detecting noise and outliers in the point cloud data. In this research, Random Sample Consensus (RANSAC) is used to determine and generate primitives geometry form from point cloud data. Those primitives geometry form generated by RANSAC can be used to contruct 3d model of the object presented in point cloud data. RANSAC method RANSAC plugin at open source software Cloud Compare can detect primitive geometries point cloud data. However there are inaccurate result in form and size of these primitive geometries that need improvement from the 3D modeler. text |
institution |
Institut Teknologi Bandung |
building |
Institut Teknologi Bandung Library |
continent |
Asia |
country |
Indonesia Indonesia |
content_provider |
Institut Teknologi Bandung |
collection |
Digital ITB |
language |
Indonesia |
description |
Point cloud data is type of data that used as raw data for 3D modelling of real object like buildings This happen because the development of survey acquisition technology such as active sensors like laser scanner and the development of image matching algorithm in photogrammetry that can produce point cloud data. Those point cloud data has position information in three dimensional coordinates. To produce 3D model, point cloud data has several steps to be done such as classification, deteksi bentuk and reconstruction.
Every object in real world is contructed by primitives geometry form such as planes, cylinder, cone, etc. The outcome that expected from deteksi bentuk process is this process can generate simplified model from point cloud data. Futhermore those model must also covered all the point cloud data and mantain it geometry as the original object. This process also can be used for detecting noise and outliers in the point cloud data.
In this research, Random Sample Consensus (RANSAC) is used to determine and generate primitives geometry form from point cloud data. Those primitives geometry form generated by RANSAC can be used to contruct 3d model of the object presented in point cloud data.
RANSAC method RANSAC plugin at open source software Cloud Compare can detect primitive geometries point cloud data. However there are inaccurate result in form and size of these primitive geometries that need improvement from the 3D modeler. |
format |
Final Project |
author |
Arthur Nabiel, Torang |
spellingShingle |
Arthur Nabiel, Torang KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
author_facet |
Arthur Nabiel, Torang |
author_sort |
Arthur Nabiel, Torang |
title |
KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
title_short |
KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
title_full |
KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
title_fullStr |
KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
title_full_unstemmed |
KAJIAN METODE RANDOM SAMPLE CONSENSUS (RANSAC) UNTUK MENDETEKSI GEOMETRI DASAR PERMUKAAN POINT CLOUD |
title_sort |
kajian metode random sample consensus (ransac) untuk mendeteksi geometri dasar permukaan point cloud |
url |
https://digilib.itb.ac.id/gdl/view/32187 |
_version_ |
1822267980824707072 |