| DOI | Resolve DOI: https://doi.org/10.1109/CRV.2018.00019 |
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| Author | Search for: Sekkati, Hicham1; Search for: Boisvert, Jonathan1; Search for: Godin, Guy1; Search for: Borgeat, Louis1 |
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| Name affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 2018 15th Conference on Computer and Robot Vision (CRV), May 8-10, 2018, Toronto, ON, Canada |
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| Subject | RGB-D; real-time fusion; large-scale 3D scans; volumetric representation; dense SLAM; GPU |
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| Abstract | This paper presents a real-time 3D shape fusion system that faithfully integrates very high resolution 3D scans with the goal of maximizing details preservation. The system fully maps complex shapes while allowing free movement similarly to dense SLAM systems in robotics where sensor fusion techniques map large environments. We propose a novel framework to integrate shapes into a volume with fine details preservation of the reconstructed shape which is an important aspect in many applications, especially for industrial inspection. The truncated signed distance function is generalized with a global variational scheme that controls edge preservation and leads to updating cumulative rules adapted for GPU implementation. The framework also embeds a map deformation method to online deform the shape and correct the system trajectory drift at few microns accuracy. Results are presented from the integrated system on two mechanical objects which illustrate the benefits of the proposed approach. |
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| Publication date | 2018-12-12 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | c02bf714-d623-42eb-aea5-f1e193c9fb62 |
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| Record created | 2019-04-26 |
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| Record modified | 2020-03-16 |
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