PARTICIPATORY IMAGE-BASED MODELS’ ALIGNMENT FOR RECONSTRUCTING A LARGE-SCALE INDOOR MAPPING
In this paper, we introduced a recently developed image-based model alignment technique for 3D reconstruction of large-scale indoor corridors. The proposed participatory model alignment technique enables crowd source single image-based modeling since it allows various participants to incorporate their images taken from different cameras for large-scale indoor mapping. This technique is robust against changes of camera orientation and prevents miss-association of a newly generated 3D model to the previously integrated models. To investigate the possibility of aligning two individual 3D models, their respective corridor topological graphs must match, and they need to geometrically transform into the same object space. Here 3D affine transformation is applied, and the transformation parameters are estimated through corresponding vertices of both 3D models. Having integrated two models in the same 3D space, they will be back projected into the image space for evaluation using Direct Linear Transformation. Note that the proposed method performs layout model matching in image space and considers information including layout topology and geometry as well as image information to address model alignment. The advantages of using layout information in the proposed alignment technique are twofold. First, a metric constraint is imposed to insure topological model consistency and balance 3D models scale issues. Second, it will reduce alignment ambiguity related to indoor corridor scenes, where the scene is enriched with multiple structural elements including various corridors junctions. To evaluate the performance of the proposed method, we have performed the experiments on a data set collected from Ross building corridors at York University. This dataset includes single images captured by a handheld wide-angle camera. The obtained results present the ability of the proposed method in alignment of single image-based 3D models while producing limited geometric errors.