UC Santa Cruz

We present a novel algorithm for geometry and camera pose reconstruction from image sequences that is specialized for indoor Manhattan scenes. Unlike general-purpose SfM/SLAM, our system represents geometric primitives in terms of canonically oriented planes. The algorithm starts by computing multi-planar segmentation and motion estimation from image pairs using constrained homographies. It then proceeds to recover the relative scale at each frame and to determine chains of match clusters, where each cluster is associated with a plane in the scene. Motion and scene geometry (expressed in terms of planar models) are then optimized using a novel formulation of Bundle Adjustment. Compared with other state-of-the-art SfM/SLAM algorithms, our technique is shown to produce superior and realistic surface reconstruction for a monocular indoor scene.