Incremental Object Database: Building 3D Models from Multiple Partial Observations
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Date
2018
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Collecting 3D object data sets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.
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published
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Book title
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Journal / series
Volume
Pages / Article No.
6835 - 6842
Publisher
IEEE
Event
25th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
03766 - Pollefeys, Marc / Pollefeys, Marc
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.
02620 - Inst. f. Robotik u. Intelligente Systeme / Inst. Robotics and Intelligent Systems
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