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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/49LD4E2
Repositorysid.inpe.br/sibgrapi/2023/08.17.20.06
Last Update2023:08.17.20.06.00 (UTC) crjung@inf.ufrgs.br
Metadata Repositorysid.inpe.br/sibgrapi/2023/08.17.20.06.01
Metadata Last Update2024:02.17.04.05.19 (UTC) administrator
DOI10.1109/SIBGRAPI59091.2023.10347129
Citation KeyFreitas:2023:ViAnTo
TitleREIS: A Visual Analytics Tool for Rendering and Exploring Instance Segmentation of Point Clouds
Short TitleREIS: A Visual Analytics Tool for Rendering and Exploring Instance Segmentation of Point Clouds
FormatOn-line
Year2023
Access Date2024, Apr. 28
Number of Files1
Size6769 KiB
2. Context
AuthorFreitas, Pedro S. de
AffiliationFederal University of Rio Grande do Sul, SENAI Innovation Institute for Integrated Solutions in Metalmechanics
EditorClua, Esteban Walter Gonzalez
Körting, Thales Sehn
Paulovich, Fernando Vieira
Feris, Rogerio
e-Mail Addresspedro.freitas@inf.ufrgs.br
Conference NameConference on Graphics, Patterns and Images, 36 (SIBGRAPI)
Conference LocationRio Grande, RS
DateNov. 06-09, 2023
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2023-08-17 20:06:01 :: crjung@inf.ufrgs.br -> administrator ::
2024-02-17 04:05:19 :: administrator -> crjung@inf.ufrgs.br :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordspoint cloud segmentation
visualization
Abstract3D Instance Segmentation (3DIS) of Point Clouds (PCs) is valuable for applications like autonomous vehicles, robotics, and Building Information Modeling (BIM). Current work on this topic is guided mainly by global metrics like mAP, which arguably do not support a deep, informed analysis of technique tradeoffs and, more importantly, directions for improvement. Qualitative analysis is widely adopted to provide such guidance, but it is generally implemented ad-hoc. This is true across many tasks in Deep Learning, but PC 3DIS is especially challenging to visually analyze due to the many variables involved: three spatial dimensions, colors, semantic labels, and instance IDs. We propose REIS, a visual analytics tool for Rendering and Exploring Instance Segmentation results. It supports qualitative analysis in two ways: first, through PC renderings targeted at efficient investigation of 3DIS results; second, by providing a systematic way to explore these results via the interactive Instance Detection Matrix- a confusion matrix analog that summarizes error and success cases, and allows the user to navigate through them. To show the efficacy of REIS, we use it to evaluate a state-of-the-art 3DIS approach on the S3DIS dataset. Our code is available at https://github.com/pedrosidra/pcloud explorer.
doc Directory Contentaccess
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/49LD4E2
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/49LD4E2
Languageen
Target File70_nocopyright.pdf
User Groupcrjung@inf.ufrgs.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
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7. Description control
e-Mail (login)crjung@inf.ufrgs.br
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