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 contain 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 objects 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. © 2012 ACM.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We acknowledge the support of a gift from Qualcomm Corporation, the Max Planck Center for Visual Computing and Communications, NSF grants 0914833 and 1011228, a KAUST AEA grant, and Marie Curie Career Integration Grant 303541.
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design