Processing large indoor scenes is a challenging task, as scan registration
and camera trajectory estimation methods accumulate errors across time. As a
result, the quality of reconstructed scans is insufficient for some
applications, such as visual-based localization and navigation, where the
correct position of walls is crucial.
For many indoor scenes, there exists an image of a technical floorplan that
contains information about the geometry and main structural elements of the
scene, such as walls, partitions, and doors. We argue that such a floorplan is
a useful source of spatial information, which can guide a 3D model
optimization.
The standard RGB-D 3D reconstruction pipeline consists of a tracking module
applied to an RGB-D sequence and a bundle adjustment (BA) module that takes the
posed RGB-D sequence and corrects the camera poses to improve consistency. We
propose a novel optimization algorithm expanding conventional BA that leverages
the prior knowledge about the scene structure in the form of a floorplan. Our
experiments on the Redwood dataset and our self-captured data demonstrate that
utilizing floorplan improves accuracy of 3D reconstructions.Comment: IROS 202