C. Kopf et al., "ReconstructMe - Towards a Full Autonomous Bust Generator", in Proc. of 5th Int. Conf. on 3D Body Scanning Technologies, Lugano, Switzerland, 2014, pp. 184-190, doi:10.15221/14.184.
ReconstructMe - Towards a Full Autonomous Bust Generator
Christoph Kopf, Christoph Heindl, Martin Ankerl, Harald Bauer, Andreas Pichler
Profactor GmbH, Steyr (Upper Austria), Austria
In the most recent years, 3D data acquisition was revolutionized by consumer grade real-time capable 3D depth cameras with appropriate data quality. A wide range of different 3D applications was developed since the release of these cameras. Especially the area of 3D body scanning became cost efficiently and the number of applications started to grow. A new trend is to generate 3D self-portraits and print them on commercial available 3D printer. This paper introduces a low-cost scanning system to generate printable 3D upper torso busts at home using commodity computation hardware.
The workflow of the system is optimized to work without the need of any manual interaction with the user and any additional persons. It's designed to generate 3D printable busts automatically which allows people without 3D knowledge to create their own 3D busts. The user just has to perform a full rotation (360 degrees) to capture the upper torso data from all views. The end of the full rotation is detected automatically. When the scanning procedure is done, a post processing routine is triggered. This routine includes cleaning the scan from noise, estimating the geometry and color of unseen areas and cutting the bust to a certain height. The final result is a watertight, scaled and oriented triangle mesh. This allows the user to put the final mesh to common 3D printer drivers and immediately print its 3D bust.
In our tests, the workflow was proven to be very robust and we were able to generate hundreds of 3D busts with high recognition value of the persons. However, our tests showed different results depending on the conditions. In this paper we will introduce our experience of how the setup can be improved and thus the quality of the results can be enhanced.
In future, this principle could be extended by using more than one sensor at the same time to capture the whole body shape of a person and to generate clean 3D full body models.
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