Color-Based Algorithm for Automatic Merging of Multiview 3D Point Clouds

Hołowko, Elwira; Wojsz, Jerzy; Sitnik, Robert; Karaszewski, Maciej

doi:10.1145/2558306

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…For this purpose, at least three pairs of corresponding points from every two point clouds are needed to calculate a transformation between the views. For example, the following Euclidean metric (λ) may be used to measure the similarity of every two points in space [11].…”

Section: B 3d Surface Reconstructionmentioning

confidence: 99%

Compressed Geometric Arrays for Point Cloud Processing

Roodaki¹,

Bojnordi²

2021

Preprint

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The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Treebased structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328× speed up, 1321× better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL). Index Terms-point cloud representations point cloud operations spatial/temporal coding memory managementHoda Roodaki is with K.

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Section: B 3d Surface Reconstructionmentioning

confidence: 99%

Compressed Geometric Arrays for Point Cloud Processing

Roodaki¹,

Bojnordi²

2021

Preprint

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“…Such elements can be used for image (or 3D data) matching and stitching using corresponding points, pattern recognition and object classification. Features can be extracted by means of intensity, hue, curvature, and other parameter analyses (Roth 1999;Lowe 2004;Mikolajczyk and Tuytelaars 2009;Hołowko et al 2014; for typical computer vision applications see: Hassaballah et al 2016).…”

Section: Featurementioning

confidence: 99%

Digital Techniques for Documenting and Preserving Cultural Heritage

2018

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This exciting series publishes both monographs and edited thematic collections in the broad areas of cultural heritage, digital humanities, collecting and collections, public history and allied areas of applied humanities. In the spirit of our mission to take a stand for the humanities, this series illustrates humanities research keeping pace with technological innovation, globalization, and democratization. We value a variety of established, new, and diverse voices and topics in humanities research and this series provides a platform for publishing the results of cuttingedge projects within these fields. LIST OF ILLUSTRATIONS and Space in Cultural Heritage (www.cosch.info), supported by the European Cooperation in Science and Technology (COST, www.cost.eu). COST is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.

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“…The great advantage of our system is that every situational or detailed scan is automatically or semi‐automatically (in some cases, by indication of one similar point on both scans) integrated geometrically with the scans obtained from a laser scanner . This means that technicians from forensic teams can see the progress of 3D crime scene documentation in real time.…”

Section: Concept Of Hierarchical Three‐dimensional Measurement Systemmentioning

confidence: 99%

“…This means that technicians from forensic teams can see the progress of 3D crime scene documentation in real time. Every time they need to refine the documentation at some point, the technicians simply have to scan the point with a situational or detailed scanner, and the result is automatically fitted into the virtual scene (Fig. ).…”

Section: Concept Of Hierarchical Three‐dimensional Measurement Systemmentioning

confidence: 99%

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Hierarchical, Three‐Dimensional Measurement System for Crime Scene Scanning

Adamczyk

Sitnik

Woźniak

2017

Journal of Forensic Sciences

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We present a new generation of three-dimensional (3D) measuring systems, developed for the process of crime scene documentation. This measuring system facilitates the preparation of more insightful, complete, and objective documentation for crime scenes. Our system reflects the actual requirements for hierarchical documentation, and it consists of three independent 3D scanners: a laser scanner for overall measurements, a situational structured light scanner for more minute measurements, and a detailed structured light scanner for the most detailed parts of tscene. Each scanner has its own spatial resolution, of 2.0, 0.3, and 0.05 mm, respectively. The results of interviews we have conducted with technicians indicate that our developed 3D measuring system has significant potential to become a useful tool for forensic technicians. To ensure the maximum compatibility of our measuring system with the standards that regulate the documentation process, we have also performed a metrological validation and designated the maximum permissible length measurement error E for each structured light scanner. In this study, we present additional results regarding documentation processes conducted during crime scene inspections and a training session.

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Color-Based Algorithm for Automatic Merging of Multiview 3D Point Clouds

Cited by 8 publications

References 22 publications

Compressed Geometric Arrays for Point Cloud Processing

Compressed Geometric Arrays for Point Cloud Processing

Digital Techniques for Documenting and Preserving Cultural Heritage

Hierarchical, Three‐Dimensional Measurement System for Crime Scene Scanning

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