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 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.
Similarly, REF used learned object models to reconstruct dense 3D models from a single scan of the indoor scene.
Acquiring 3D indoor environments with variability and repetition
TOGS
Computer Science