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Acquiring 3D Indoor Environments with Variability and Repetition
| Venue: | ACM Transactions on Graphics (TOG |
| Citations: | 41 - 7 self |
BibTeX
@ARTICLE{Kim_acquiring3d,
author = {Young Min Kim and Niloy J. Mitra and Dong-ming Yan and Leonidas Guibas},
title = {Acquiring 3D Indoor Environments with Variability and Repetition},
journal = {ACM Transactions on Graphics (TOG},
year = {},
pages = {2012}
}
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Abstract
input single-view scan recognized objects retrieved and posed models office scene Figure 1: (Left) Given a single view scan of a 3D environment obtained using a fast range scanner, we perform scene understanding by recognizing repeated objects, while factoring out their modes of variability (middle). The repeating objects have been learned beforehand as low complexity models, along with their joint deformations. We extract the objects despite a poor quality input scan with large missing parts and many outliers. The extracted parameters can then be used to pose 3D models to create a plausible scene reconstruction (right). Large-scale acquisition of exterior urban environments is by now a well-established technology, supporting many applications in search, navigation, and commerce. The same is, however, not the case for indoor environments, where access is often restricted and the spaces are cluttered. Further, such environments typically con-tain a high density of repeated objects (e.g., tables, chairs, monitors, etc.) in regular or non-regular arrangements with significant pose variations and articulations. In this paper, we exploit the special structure of indoor environments to accelerate their 3D acquisition and recognition with a low-end handheld scanner. Our approach runs in two phases: (i) a learning phase wherein we acquire 3D models of frequently occurring objects and capture their variability modes from only a few scans, and (ii) a recognition phase wherein from a single scan of a new area, we identify previously seen ob-jects but in different poses and locations at an average recognition time of 200ms/model. We evaluate the robustness and limits of the proposed recognition system using a range of synthetic and real world scans under challenging settings.
Keyphrases
indoor environment repeated object many application single scan extracted parameter recognition phase wherein large-scale acquisition special structure poor quality input low-end handheld scanner different pose significant pose variation average recognition time well-established technology exterior urban environment model office scene low complexity model recognition system plausible scene reconstruction joint deformation real world scan non-regular arrangement many outlier single view scan fast range scanner high density scene understanding variability mode new area learning phase wherein
