Incremental Object Database: Building 3D Models from Multiple Partial Observations
This addresses the time-consuming and labor-intensive process of 3D data collection for robotics or AR applications, but it is incremental as it builds on existing segmentation and matching techniques.
The paper tackles the problem of manually collecting 3D object datasets by presenting a system that incrementally builds a database of objects from partial observations as a mobile agent moves through a scene, enabling the creation and improvement of object models on the fly and reconstructing unobserved parts.
Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.