Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds

2020 ◽  
Author(s):  
Vladislav Golyanik
Keyword(s):  
3D Reconstruction ◽  
Point Clouds ◽  
Robust Methods

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

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

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.).

scholarly journals A comparison of multi-view 3D reconstruction of a rock wall using several cameras and a laser scanner

2014 ◽  
Vol XL-5 ◽  
pp. 573-580 ◽  
Author(s):  
K. Thoeni ◽  
A. Giacomini ◽  
R. Murtagh ◽  
E. Kniest
Keyword(s):  
3D Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Ground Truth ◽  
Point Clouds ◽  
Digital Cameras ◽  
Rock Wall ◽  
Geological Features ◽  
Dense Point ◽  
Sharp Edges

This work presents a comparative study between multi-view 3D reconstruction using various digital cameras and a terrestrial laser scanner (TLS). Five different digital cameras were used in order to estimate the limits related to the camera type and to establish the minimum camera requirements to obtain comparable results to the ones of the TLS. The cameras used for this study range from commercial grade to professional grade and included a GoPro Hero 1080 (5 Mp), iPhone 4S (8 Mp), Panasonic Lumix LX5 (9.5 Mp), Panasonic Lumix ZS20 (14.1 Mp) and Canon EOS 7D (18 Mp). The TLS used for this work was a FARO Focus 3D laser scanner with a range accuracy of ±2 mm. The study area is a small rock wall of about 6 m height and 20 m length. The wall is partly smooth with some evident geological features, such as non-persistent joints and sharp edges. Eight control points were placed on the wall and their coordinates were measured by using a total station. These coordinates were then used to georeference all models. A similar number of images was acquired from a distance of between approximately 5 to 10 m, depending on field of view of each camera. The commercial software package PhotoScan was used to process the images, georeference and scale the models, and to generate the dense point clouds. Finally, the open-source package CloudCompare was used to assess the accuracy of the multi-view results. Each point cloud obtained from a specific camera was compared to the point cloud obtained with the TLS. The latter is taken as ground truth. The result is a coloured point cloud for each camera showing the deviation in relation to the TLS data. The main goal of this study is to quantify the quality of the multi-view 3D reconstruction results obtained with various cameras as objectively as possible and to evaluate its applicability to geotechnical problems.

scholarly journals THREE DIMENTIONAL RECONSTRUCTION OF LARGE CULTURAL HERITAGE OBJECTS BASED ON UAV VIDEO AND TLS DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 985-988 ◽  
Author(s):  
Z. Xu ◽  
T. H. Wu ◽  
Y. Shen ◽  
L. Wu
Keyword(s):  
3D Reconstruction ◽  
Cultural Heritage ◽  
Laser Scanner ◽  
Digital Camera ◽  
Point Clouds ◽  
Endurance Capacity ◽  
Still Images ◽  
Culture Heritage ◽  
Invariant Regions ◽  
Cultural Heritage Objects

This paper investigates the synergetic use of unmanned aerial vehicle (UAV) and terrestrial laser scanner (TLS) in 3D reconstruction of cultural heritage objects. Rather than capturing still images, the UAV that equips a consumer digital camera is used to collect dynamic videos to overcome its limited endurance capacity. Then, a set of 3D point-cloud is generated from video image sequences using the automated structure-from-motion (SfM) and patch-based multi-view stereo (PMVS) methods. The TLS is used to collect the information that beyond the reachability of UAV imaging e.g., partial building facades. A coarse to fine method is introduced to integrate the two sets of point clouds UAV image-reconstruction and TLS scanning for completed 3D reconstruction. For increased reliability, a variant of ICP algorithm is introduced using local terrain invariant regions in the combined designation. The experimental study is conducted in the Tulou culture heritage building in Fujian province, China, which is focused on one of the TuLou clusters built several hundred years ago. Results show a digital 3D model of the Tulou cluster with complete coverage and textural information. This paper demonstrates the usability of the proposed method for efficient 3D reconstruction of heritage object based on UAV video and TLS data.

scholarly journals GEOMETRIC POINT QUALITY ASSESSMENT FOR THE AUTOMATED, MARKERLESS AND ROBUST REGISTRATION OF UNORDERED TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 89-96 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Incidence Angle ◽  
Point Clouds ◽  
Urban Environments ◽  
Coordinate Frame ◽  
Consistency Check ◽  
Geometric Point ◽  
Feature Correspondences ◽  
Urban Accessibility

The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.

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