HyBoDT: Hybrid Bounded Distance Transforms of Trimmed NURBS Models

2021 ◽  
pp. 1-11
Author(s):  
Aditya Balu ◽  
Sambit Ghadai ◽  
Onur Rauf Bingol ◽  
Adarsh Krishnamurthy
Keyword(s):  
Field Representation ◽  
Distance Transforms ◽  
3D Space ◽  
Distance Fields ◽  
Bounded Distance ◽  
Nurbs Surfaces ◽  
Discrete Representations ◽  
Graphics Rendering ◽  
2D And 3D ◽  
Voxel Grid

Abstract Distance field representation of objects in 3D space has several applications such as shape manipulation, graphics rendering, path planning, etc. Distance transforms (DTs) are discrete representations of distance fields in a regular voxel grid. The two main limitations of using distance transforms are that they are compute-intensive, and there are errors introduced while representing the object using DTs. In this work, we develop an hybrid GPU-accelerated marching wavefront method for computing DTs of models composed of trimmed NURBS surfaces with theoretical bounds. Our hybrid marching approach eliminates the error due to calculating approximate distances by marching. We also calculate the bounds on the error introduced due to the tessellation of the trimmed NURBS surfaces and calculate the propagation of these bounds in computing the DT. Finally, we present computation times for both 2D and 3D GPU DTs of test objects. We show that our GPU-accelerated approach is significantly faster than existing CPU-based methods.

scholarly journals Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chunlei Xia ◽  
Longwen Fu ◽  
Zuoyi Liu ◽  
Hui Liu ◽  
Lingxin Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Aquatic Toxicity ◽  
Warning System ◽  
Video Tracking ◽  
Behavioral Data ◽  
3D Space ◽  
Biological Early Warning System ◽  
Abnormal Pattern ◽  
2D And 3D

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

scholarly journals Gold Exploration in Two and Three Dimensions: Improved and Correlative Insights from Microscopy and X-Ray Computed Tomography

Minerals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 476
Author(s):  
Joshua Chisambi ◽  
Bjorn von der Heyden ◽  
Muofhe Tshibalanganda ◽  
Stephan Le Roux
Keyword(s):  
Computed Tomography ◽  
Spatial Distribution ◽  
Gold Mineralization ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Low Grade ◽  
X Ray ◽  
3D Space ◽  
X Ray Computed ◽  
2D And 3D

In this contribution, we highlight a correlative approach in which three-dimensional structural/positional data are combined with two dimensional chemical and mineralogical data to understand a complex orogenic gold mineralization system; we use the Kirk Range (southern Malawi) as a case study. Three dimensional structures and semi-quantitative mineral distributions were evaluated using X-ray Computed Tomography (XCT) and this was augmented with textural, mineralogical and chemical imaging using Scanning Electron Microscopy (SEM) and optical microscopy as well as fire assay. Our results detail the utility of the correlative approach both for quantifying gold concentrations in core samples (which is often nuggety and may thus be misrepresented by quarter- or half-core assays), and for understanding the spatial distribution of gold and associated structures and microstructures in 3D space. This approach overlays complementary datasets from 2D and 3D analytical protocols, thereby allowing a better and more comprehensive understanding on the distribution and structures controlling gold mineralization. Combining 3D XCT analyses with conventional 2D microscopies derive the full value out of a given exploration drilling program and it provides an excellent tool for understanding gold mineralization. Understanding the spatial distribution of gold and associated structures and microstructures in 3D space holds vast potential for exploration practitioners, especially if the correlative approach can be automated and if the resultant spatially-constrained microstructural information can be fed directly into commercially available geological modelling software. The extra layers of information provided by using correlative 2D and 3D microscopies offer an exciting new tool to enhance and optimize mineral exploration workflows, given that modern exploration efforts are targeting increasingly complex and low-grade ore deposits.

scholarly journals AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality

2019 ◽  
Vol 11 (19) ◽  
pp. 2243 ◽  
Author(s):  
Weiquan Liu ◽  
Cheng Wang ◽  
Xuesheng Bian ◽  
Shuting Chen ◽  
Wei Li ◽  
...  
Keyword(s):  
Image Matching ◽  
Point Cloud ◽  
Spatial Relationship ◽  
3D Image ◽  
Image Patch ◽  
3D Space ◽  
Cross Domain ◽  
Feature Descriptors ◽  
Outdoor Environments ◽  
2D And 3D

Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual–real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual–real registration to AR in outdoor environments.

Discrete Skeletons from Distance Transforms in 2D and 3D

2008 ◽  
pp. 155-190 ◽  
Author(s):  
Gunilla Borgefors ◽  
Ingela Nyström ◽  
Gabriella Sanniti di Baja
Keyword(s):  
Distance Transforms ◽  
2D And 3D

Path Point Calculation and 3D Coordinate Display for WMR Based on Vision

2011 ◽  
Vol 121-126 ◽  
pp. 4176-4179
Author(s):  
Hong Wei Gao ◽  
Chang Yi Luan ◽  
Hui Ying Yang
Keyword(s):  
Mobile Robot ◽  
Wheeled Mobile Robot ◽  
Simulation Platform ◽  
Motion Simulation ◽  
3D Space ◽  
Point Calculation ◽  
Simulation Results ◽  
2D And 3D ◽  
Path Point

According to the path point calculation, display and motion simulaiton problem for wheeled mobile robot(WMR), two kinds of path point calcualtion method are discussed from 2D and 3D space in this paper. The simulation results based on the motion simulation platform prove the validity and practicability of the proposed method.

scholarly journals Colored anchored visibility representations in 2D and 3D space

2020 ◽  
Vol 89 ◽  
pp. 101592
Author(s):  
Carla Binucci ◽  
Emilio Di Giacomo ◽  
Seok-Hee Hong ◽  
Giuseppe Liotta ◽  
Henk Meijer ◽  
...  
Keyword(s):  
3D Space ◽  
Visibility Representations ◽  
2D And 3D

scholarly journals Comparing Two Different Automatic Methods to Measure Femoral Neck-Shaft Angle Based on PointNet++ Network

2021 ◽  
Author(s):  
Zhe Li ◽  
Jiayu Yang ◽  
Xinghua Li ◽  
Kunzheng Wang ◽  
Jungang Han ◽  
...  
Keyword(s):  
Femoral Neck ◽  
Femoral Shaft ◽  
Femoral Neck Axis ◽  
Neck Shaft Angle ◽  
3D Space ◽  
Significant Difference ◽  
Shaft Angle ◽  
Automatic Methods ◽  
3D Methods ◽  
2D And 3D

Abstract Bacnground: Accurate measurement of the femoral neck-shaft angle (NSA) is of great significance for diagnosing hip joint diseases and preoperative planning of total hip arthroplasty. However, the fitting lines of the femoral neck and femoral shaft did not always intersect in 3D space. Thus, it is unclear whether there is a difference between 2D and 3D methods for measuring NSA. Methods: The femoral point cloud datasets from 310 subjects were segmented into three regions, including the femoral head, femoral neck, and femoral shaft using PointNet++. We created a projection plane to simulate the hip anteroposterior radiograph and fitted the femoral neck axis and femoral shaft axis to complete the 2D measurement, while we directly fitted the two axes in space to complete the 3D measurement. Also, we conducted the manual measurement of the NSA. We verified the accuracy of the segmentation and compared the results of the two automatic and manual methods. Results: The Dice coefficient of femoral segmentation reached 0.9746, and MIoU of that was 0.9165. No significant difference was found between any two of the three methods. While comparing the 2D and 3D methods, the average accuracy was 98.00%, and the average error was 2.58°. Conclusion: This paper proposed two accurate and automatic methods to measure the NSA based on a 2D plane and a 3D model respectively. Although the femoral neck and femoral shaft axes did not intersect in 3D space, the NSAs obtained by 2D and 3D methods were basically consistent.

Mapping Biomolecular Sequences: Graphical Representations - their Origins, Applications and Future Prospects

2021 ◽  
Vol 24 ◽  
Author(s):  
Ashesh Nandy
Keyword(s):  
Dna Sequences ◽  
Graphical Representation ◽  
Sequence Data ◽  
Basic Unit ◽  
Graphical Representations ◽  
Biological Sequences ◽  
Biological Sequence ◽  
New Approach ◽  
3D Space ◽  
2D And 3D

The exponential growth in the depositories of biological sequence data have generated an urgent need to store, retrieve and analyse the data efficiently and effectively for which the standard practice of using alignment procedures are not adequate due to high demand on computing resources and time. Graphical representation of sequences has become one of the most popular alignment-free strategies to analyse the biological sequences where each basic unit of the sequences – the bases adenine, cytosine, guanine and thymine for DNA/RNA, and the 20 amino acids for proteins – are plotted on a multi-dimensional grid. The resulting curve in 2D and 3D space and the implied graph in higher dimensions provide a perception of the underlying information of the sequences through visual inspection; numerical analyses, in geometrical or matrix terms, of the plots provide a measure of comparison between sequences and thus enable study of sequence hierarchies. The new approach has also enabled studies of comparisons of DNA sequences over many thousands of bases and provided new insights into the structure of the base compositions of DNA sequences In this article we review in brief the origins and applications of graphical representations and highlight the future perspectives in this field.

scholarly journals BIM-BASED INDOOR PATH PLANNING CONSIDERING OBSTACLES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 417-423 ◽  
Author(s):  
M. Xu ◽  
S. Wei ◽  
S. Zlatanova ◽  
R. Zhang
Keyword(s):  
Path Planning ◽  
Semantic Information ◽  
Indoor Navigation ◽  
Triangular Prism ◽  
Building Structures ◽  
Indoor Space ◽  
Space Subdivision ◽  
3D Space ◽  
Building Components ◽  
2D And 3D

At present, 87 % of people’s activities are in indoor environment; indoor navigation has become a research issue. As the building structures for people’s daily life are more and more complex, many obstacles influence humans’ moving. Therefore it is essential to provide an accurate and efficient indoor path planning. Nowadays there are many challenges and problems in indoor navigation. Most existing path planning approaches are based on 2D plans, pay more attention to the geometric configuration of indoor space, often ignore rich semantic information of building components, and mostly consider simple indoor layout without taking into account the furniture. Addressing the above shortcomings, this paper uses BIM (IFC) as the input data and concentrates on indoor navigation considering obstacles in the multi-floor buildings. After geometric and semantic information are extracted, 2D and 3D space subdivision methods are adopted to build the indoor navigation network and to realize a path planning that avoids obstacles. The 3D space subdivision is based on triangular prism. The two approaches are verified by the experiments.

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