The method proposed in this paper is the first to combine the benefits of both approaches, supporting online reconstruction of scenes with hundreds of millions of samples from high-resolution sensing modalities such as structured light or laser scanners. The key property of our algorithm is that it sidesteps the signed-distance computation of classical reconstruction techniques in favor of direct filtering, parametrization, and mesh and texture extraction. All of these steps can be realized using only weak notions of spatial neighborhoods, which allows for an implementation that scales approximately linearly with the size of each dataset that is integrated into a partial reconstruction. Combined, these algorithmic differences enable a drastically more efficient output-driven interactive scanning and reconstruction workflow, where the user is able to see the final quality field-aligned textured mesh during the entirety of the scanning procedure. Holes or parts with registration problems are displayed in real-time to the user and can be easily resolved by adding further localized scans, or by adjusting the input point cloud using our interactive editing tools with immediate visual feedback on the output mesh.
We demonstrate the effectiveness of our algorithm in conjunction with a state-of-the-art structured light scanner and optical tracking system and test it on a large variety of challenging models.
Research Seminar Announcement
In the course of the Visual Computing research seminar Prof. Dr. Mario Botsch from the University of Bielefeld will give a talk with subsequent discussion about the topic ICSPACE: Motor Learning in Virtual Reality.
We would like to invite everyone interested to join us. The research seminar will take place on Friday 24.11.2017, 13:15 in G29-R335.