Fast and Robust 3D Reconstruction Solution from Permissive Open-Source Code

Victor Gouveia de M. Lyra; Adam H. M. Pinto; Gustavo C. R. Lima; João Paulo Lima; Veronica Teichrieb; Jonysberg Peixoto Quintino; Fabio Q. B. da Silva; André L M Santos; Helder Pinho

doi:10.5753/jis.2021.2065

Fast and Robust 3D Reconstruction Solution from Permissive Open-Source Code

Journal on Interactive Systems ◽

10.5753/jis.2021.2065 ◽

2021 ◽

Vol 12 (1) ◽

pp. 206-218

Author(s):

Victor Gouveia de M. Lyra ◽

Adam H. M. Pinto ◽

Gustavo C. R. Lima ◽

João Paulo Lima ◽

Veronica Teichrieb ◽

...

Keyword(s):

3D Reconstruction ◽

Virtual Environments ◽

Structure From Motion ◽

Point Cloud ◽

State Of The Art ◽

Source Code ◽

Texture Mapping ◽

Large Datasets ◽

Stereo Reconstruction ◽

Object Models

With the growth of access to faster computers and more powerful cameras, the 3D reconstruction of objects has become one of the public's main topics of research and demand. This task is vigorously applied in creating virtual environments, creating object models, and other activities. One of the techniques for obtaining 3D features is photogrammetry, mapping objects and scenarios using only images. However, this process is very costly and can be pretty time-consuming for large datasets. This paper proposes a robust, efficient reconstruction pipeline with a low runtime in batch processing and permissive code. It is even possible to commercialize it without the need to keep the code open. We mix an improved structure from motion algorithm and a recurrent multi-view stereo reconstruction. We also use the Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compare our results with state-of-the-art techniques using benchmarks and our datasets. The results showed a decrease of 69.4% in the average execution time, with high quality but a greater need for more images to achieve complete reconstruction.

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Parallel RANSAC for Point Cloud Registration

Foundations of Computing and Decision Sciences ◽

10.1515/fcds-2017-0010 ◽

2017 ◽

Vol 42 (3) ◽

pp. 203-217 ◽

Cited By ~ 1

Author(s):

Daniel Koguciuk

Keyword(s):

Point Cloud ◽

State Of The Art ◽

Local Coordinate System ◽

Source Code ◽

Comparative Test ◽

The Internet ◽

Program Execution ◽

Point Cloud Registration ◽

Cuda Architecture ◽

Ransac Algorithm

AbstractIn this paper, a project and implementation of the parallel RANSAC algorithm in CUDA architecture for point cloud registration are presented. At the beginning, a serial state of the art method with several heuristic improvements from the literature compared to basic RANSAC is introduced. Subsequently, its algorithmic parallelization and CUDA implementation details are discussed. The comparative test has proven a significant program execution acceleration. The result is finding of the local coordinate system of the object in the scene in the near real-time conditions. The source code is shared on the Internet as a part of the Heuros system.

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Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision

Applied Sciences ◽

10.3390/app11177961 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7961

Author(s):

Ning Lv ◽

Chengyu Wang ◽

Yujing Qiao ◽

Yongde Zhang

Keyword(s):

3D Printing ◽

3D Reconstruction ◽

Point Cloud ◽

Cloud Model ◽

Texture Mapping ◽

Open Loop ◽

Reconstruction Method ◽

Sift Algorithm ◽

Printing Process ◽

Process Detection

The 3D printing process lacks real-time inspection, which is still an open-loop manufacturing process, and the molding accuracy is low. Based on the 3D reconstruction theory of machine vision, in order to meet the applicability requirements of 3D printing process detection, a matching fusion method is proposed. The fast nearest neighbor (FNN) method is used to search matching point pairs. The matching point information of FFT-SIFT algorithm based on fast Fourier transform is superimposed with the matching point information of AKAZE algorithm, and then fused to obtain more dense feature point matching information and rich edge feature information. Combining incremental SFM algorithm with global SFM algorithm, an integrated SFM sparse point cloud reconstruction method is developed. The dense point cloud is reconstructed by PMVs algorithm, the point cloud model is meshed by Delaunay triangulation, and then the accurate 3D reconstruction model is obtained by texture mapping. The experimental results show that compared with the classical SIFT algorithm, the speed of feature extraction is increased by 25.0%, the number of feature matching is increased by 72%, and the relative error of 3D reconstruction results is about 0.014%, which is close to the theoretical error.

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PHOTOGRAMMETRIC 3D RECONSTRUCTION IN MATLAB: DEVELOPMENT OF A FREE TOOL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w8-141-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 141-145 ◽

Cited By ~ 1

Author(s):

A. Masiero

Keyword(s):

3D Reconstruction ◽

Structure From Motion ◽

Point Cloud ◽

Feature Matching ◽

Computational Burden ◽

Matching Process ◽

Current State ◽

Dense Point ◽

The University

This paper presents the current state of development of a free Matlab tool for photogrammetric reconstruction developed at the University of Padova, Italy. The goal of this software is mostly educational, i.e. allowing students to have a close look to the specific steps which lead to the computation of a dense point cloud. As most of recently developed photogrammetric softwares, it is based on a Structure from Motion approach. Despite being mainly motivated by educational purposes, certain implementation details are clearly inspired by recent research works, e.g. limiting the computational burden of the feature matching by determining a suboptimal set of features to be considered, using information provided by external sensors to ease the matching process.

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Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽

10.3390/f12020250 ◽

2021 ◽

Vol 12 (2) ◽

pp. 250

Author(s):

Wade T. Tinkham ◽

Neal C. Swayze

Keyword(s):

Structure From Motion ◽

Ponderosa Pine ◽

Point Cloud ◽

Image Resolution ◽

Canopy Height ◽

Computer Hardware ◽

Forest Monitoring ◽

Individual Tree ◽

Ponderosa Pine Forests ◽

Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

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Image Restoration by Learning Morphological Opening-Closing Network

Mathematical Morphology - Theory and Applications ◽

10.1515/mathm-2020-0103 ◽

2020 ◽

Vol 4 (1) ◽

pp. 87-107

Author(s):

Ranjan Mondal ◽

Moni Shankar Dey ◽

Bhabatosh Chanda

Keyword(s):

Neural Network ◽

Image Restoration ◽

State Of The Art ◽

Source Code ◽

Back Propagation ◽

Image Features ◽

Main Difficulty ◽

The Right ◽

Right Order ◽

Morphological Opening

AbstractMathematical morphology is a powerful tool for image processing tasks. The main difficulty in designing mathematical morphological algorithm is deciding the order of operators/filters and the corresponding structuring elements (SEs). In this work, we develop morphological network composed of alternate sequences of dilation and erosion layers, which depending on learned SEs, may form opening or closing layers. These layers in the right order along with linear combination (of their outputs) are useful in extracting image features and processing them. Structuring elements in the network are learned by back-propagation method guided by minimization of the loss function. Efficacy of the proposed network is established by applying it to two interesting image restoration problems, namely de-raining and de-hazing. Results are comparable to that of many state-of-the-art algorithms for most of the images. It is also worth mentioning that the number of network parameters to handle is much less than that of popular convolutional neural network for similar tasks. The source code can be found here https://github.com/ranjanZ/Mophological-Opening-Closing-Net

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Efficient Directed Densest Subgraph Discovery

ACM SIGMOD Record ◽

10.1145/3471485.3471494 ◽

2021 ◽

Vol 50 (1) ◽

pp. 33-40

Author(s):

Chenhao Ma ◽

Yixiang Fang ◽

Reynold Cheng ◽

Laks V.S. Lakshmanan ◽

Wenjie Zhang ◽

...

Keyword(s):

Approximation Algorithms ◽

State Of The Art ◽

Exact Algorithms ◽

Large Datasets ◽

Dense Subgraph ◽

Extensive Evaluation ◽

Wide Range ◽

Edge Graph ◽

Community Mining ◽

Densest Subgraph

Given a directed graph G, the directed densest subgraph (DDS) problem refers to the finding of a subgraph from G, whose density is the highest among all the subgraphs of G. The DDS problem is fundamental to a wide range of applications, such as fraud detection, community mining, and graph compression. However, existing DDS solutions suffer from efficiency and scalability problems: on a threethousand- edge graph, it takes three days for one of the best exact algorithms to complete. In this paper, we develop an efficient and scalable DDS solution. We introduce the notion of [x, y]-core, which is a dense subgraph for G, and show that the densest subgraph can be accurately located through the [x, y]-core with theoretical guarantees. Based on the [x, y]-core, we develop both exact and approximation algorithms. We have performed an extensive evaluation of our approaches on eight real large datasets. The results show that our proposed solutions are up to six orders of magnitude faster than the state-of-the-art.

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An Efficient and General Framework for Aerial Point Cloud Classification in Urban Scenarios

Remote Sensing ◽

10.3390/rs13101985 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1985

Author(s):

Emre Özdemir ◽

Fabio Remondino ◽

Alessandro Golkar

Keyword(s):

Point Cloud ◽

State Of The Art ◽

Computational Power ◽

Specific Data ◽

Cloud Processing ◽

Point Cloud Processing ◽

Current State ◽

Data Source ◽

Point Cloud Classification ◽

And Training

With recent advances in technologies, deep learning is being applied more and more to different tasks. In particular, point cloud processing and classification have been studied for a while now, with various methods developed. Some of the available classification approaches are based on specific data source, like LiDAR, while others are focused on specific scenarios, like indoor. A general major issue is the computational efficiency (in terms of power consumption, memory requirement, and training/inference time). In this study, we propose an efficient framework (named TONIC) that can work with any kind of aerial data source (LiDAR or photogrammetry) and does not require high computational power while achieving accuracy on par with the current state of the art methods. We also test our framework for its generalization ability, showing capabilities to learn from one dataset and predict on unseen aerial scenarios.

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A Benchmark and Evaluation of Non-Rigid Structure from Motion

International Journal of Computer Vision ◽

10.1007/s11263-020-01406-y ◽

2020 ◽

Author(s):

Sebastian Hoppe Nesgaard Jensen ◽

Mads Emil Brix Doest ◽

Henrik Aanæs ◽

Alessio Del Bue

Keyword(s):

Computer Vision ◽

Structure From Motion ◽

State Of The Art ◽

The State ◽

Quality Data ◽

Data Set ◽

Rigid Structure ◽

Public Data ◽

3D Information ◽

Further Development

AbstractNon-rigid structure from motion (nrsfm), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of nrsfm, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse nrsfm. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.

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