Normal estimation of fuzzy inference for mass point clouds of irregular model in the 3D reconstruction

2014 ◽  
Author(s):  
Yan Ju Liu ◽  
Jin Gang Jiang ◽  
Feng Juang Miao ◽  
Bai Rui Tao ◽  
Hong Lie Zhang
Keyword(s):  
3D Reconstruction ◽  
Fuzzy Inference ◽  
Point Clouds ◽  
Mass Point ◽  
Normal Estimation

Normal Estimation for Mass Point Clouds of Irregular Model in the 3D Reconstruction based on Fuzzy Inference

2014 ◽  
Vol 7 (5) ◽  
pp. 131-138
Author(s):  
Liu Yan-ju ◽  
Jiang Jin-gang ◽  
Miao Feng-juan ◽  
Tao Bai-rui ◽  
Zhang Hong-lie
Keyword(s):  
3D Reconstruction ◽  
Fuzzy Inference ◽  
Point Clouds ◽  
Mass Point ◽  
Normal Estimation

scholarly journals Geological Modeling Method Based on the Normal Dynamic Estimation of Sparse Point Clouds

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1819
Author(s):  
Tiandong Shi ◽  
Deyun Zhong ◽  
Liguan Wang
Keyword(s):  
Point Cloud ◽  
Minimum Spanning Tree ◽  
Point Clouds ◽  
Original Method ◽  
Modeling Method ◽  
Geological Modeling ◽  
Improved Method ◽  
Normal Estimation ◽  
Sampling Points ◽  
Correct Point

The effect of geological modeling largely depends on the normal estimation results of geological sampling points. However, due to the sparse and uneven characteristics of geological sampling points, the results of normal estimation have great uncertainty. This paper proposes a geological modeling method based on the dynamic normal estimation of sparse point clouds. The improved method consists of three stages: (1) using an improved local plane fitting method to estimate the normals of the point clouds; (2) using an improved minimum spanning tree method to redirect the normals of the point clouds; (3) using an implicit function to construct a geological model. The innovation of this method is an iterative estimation of the point cloud normal. The geological engineer adjusts the normal direction of some point clouds according to the geological law, and then the method uses these correct point cloud normals as a reference to estimate the normals of all point clouds. By continuously repeating the iterative process, the normal estimation result will be more accurate. Experimental results show that compared with the original method, the improved method is more suitable for the normal estimation of sparse point clouds by adjusting normals, according to prior knowledge, dynamically.

3D Reconstruction of Large Point Clouds with a New Point Correspondence Algorithm

2012 ◽  
Author(s):  
Rogério Yugo Takimoto ◽  
Renato Vogelaar ◽  
Edson Kenji Ueda ◽  
Marcos S.G. Tsuzuki ◽  
Toshiyuki Gotoh ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Point Correspondence

An Iterative Closest Point Algorithm Based on Biunique Correspondence of Point Clouds for 3D Reconstruction

2015 ◽  
Vol 35 (5) ◽  
pp. 0515003 ◽  
Author(s):  
韦盛斌 Wei Shengbin ◽  
王少卿 Wang Shaoqing ◽  
周常河 Zhou Changhe ◽  
刘昆 Liu Kun ◽  
范鑫 Fan Xin
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Iterative Closest Point Algorithm

Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference

2020 ◽  
Vol 160 ◽  
pp. 149-166 ◽  
Author(s):  
Heidar Rastiveis ◽  
Alireza Shams ◽  
Wayne A. Sarasua ◽  
Jonathan Li
Keyword(s):  
Fuzzy Inference ◽  
Point Clouds ◽  
Automated Extraction

CROWD-SOURCED VISUAL DATA COLLECTION FOR MONITORING INDOOR CONSTRUCTION IN 3D

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fouad Amer ◽  
Mani Golparvar-Fard
Keyword(s):  
Data Collection ◽  
3D Reconstruction ◽  
Structure From Motion ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Visual Data ◽  
Mapping Algorithm ◽  
3D Point Clouds ◽  
Localization And Mapping

Complete and accurate 3D monitoring of indoor construction progress using visual data is challenging. It requires (a) capturing a large number of overlapping images, which is time-consuming and labor-intensive to collect, and (b) processing using Structure from Motion (SfM) algorithms, which can be computationally expensive. To address these inefficiencies, this paper proposes a hybrid SfM-SLAM 3D reconstruction algorithm along with a decentralized data collection workflow to map indoor construction work locations in 3D and any desired frequency. The hybrid 3D reconstruction method is composed of a pipeline of Structure from Motion (SfM) coupled with Multi-View Stereo (MVS) to generate 3D point clouds and a SLAM (Simultaneous Localization and Mapping) algorithm to register the separately formed models together. Our SfM and SLAM pipelines are built on binary Oriented FAST and Rotated BRIEF (ORB) descriptors to tightly couple these two separate reconstruction workflows and enable fast computation. To elaborate the data capture workflow and validate the proposed method, a case study was conducted on a real-world construction site. Compared to state-of-the-art methods, our preliminary results show a decrease in both registration error and processing time, demonstrating the potential of using daily images captured by different trades coupled with weekly walkthrough videos captured by a field engineer for complete 3D visual monitoring of indoor construction operations.

Automated 3D Reconstruction of Interiors from Point Clouds

2010 ◽  
Vol 8 (1) ◽  
pp. 55-73 ◽  
Author(s):  
Angela Budroni ◽  
Jan Boehm
Keyword(s):  
3D Reconstruction ◽  
Point Clouds

scholarly journals 3D DOCUMENTATION OF 40 KILOMETERS OF HISTORICAL PORTICOES – THE CHALLENGE

2016 ◽  
Vol XLI-B5 ◽  
pp. 711-718 ◽  
Author(s):  
F. Remondino ◽  
M. Gaiani ◽  
F. Apollonio ◽  
A. Ballabeni ◽  
M. Ballabeni ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Level Of Detail ◽  
Digital Products ◽  
3D Documentation ◽  
Colored Point ◽  
Terrestrial Photogrammetry ◽  
The Web ◽  
Open Issues ◽  
Image Datasets

In the last years the image-based pipeline for 3D reconstruction purposes has received large interest leading to fully automated methodologies able to process large image datasets and deliver 3D products with a level of detail and precision variable according to the applications. Different open issues still exist, in particular when dealing with the 3D surveying and modeling of large and complex scenarios, like historical porticoes. The paper presents an evaluation of various surveying methods for the geometric documentation of ca 40km of historical porticoes in Bologna (Italy). Finally, terrestrial photogrammetry was chosen as the most flexible and productive technique in order to deliver 3D results in form of colored point clouds or textured 3D meshes accessible on the web. The presented digital products are a complementary material for the final candidature of the porticoes as UNESCO WHS.

scholarly journals Evaluation of feature-based methods for automated network orientation

2014 ◽  
Vol XL-5 ◽  
pp. 47-54 ◽  
Author(s):  
F.I. Apollonio ◽  
A. Ballabeni ◽  
M. Gaiani ◽  
F. Remondino
Keyword(s):  
Image Processing ◽  
3D Reconstruction ◽  
Processing Time ◽  
Point Clouds ◽  
Large Networks ◽  
Sift Algorithm ◽  
3D Point Clouds ◽  
Network Orientation ◽  
Feature Based ◽  
Automated Image Processing

Every day new tools and algorithms for automated image processing and 3D reconstruction purposes become available, giving the possibility to process large networks of unoriented and markerless images, delivering sparse 3D point clouds at reasonable processing time. In this paper we evaluate some feature-based methods used to automatically extract the tie points necessary for calibration and orientation procedures, in order to better understand their performances for 3D reconstruction purposes. The performed tests – based on the analysis of the SIFT algorithm and its most used variants – processed some datasets and analysed various interesting parameters and outcomes (e.g. number of oriented cameras, average rays per 3D points, average intersection angles per 3D points, theoretical precision of the computed 3D object coordinates, etc.).

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