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Structured Global Registration of RGB-D Scans in Indoor Environments

ABSTRACT

RGB-D scanning of indoor environments (offices, homes, museums, etc.) is important for a variety of applications, including on-line real estate, virtual tourism, and virtual reality. To support these applications, we must register the RGB-D images acquired with an untracked, hand-held camera into a globally consistent and accurate 3D model. Current methods work effectively for small environments with trackable features, but often fail to reproduce large-scale structures (e.g., straight walls along corridors) or long-range relationships (e.g., parallel opposing walls in an office). In this paper, we investigate the idea of integrating a structural model into the global registration process. We introduce a fine-to-coarse algorithm that detects planar structures spanning multiple RGB-D frames and establishes geometric constraints between them as they become aligned. Detection and enforcement of these structural constraints in the inner loop of a global registration algorithm guides the solution towards more accurate global registrations, even without detecting loop closures. During experiments with a newly created benchmark for the SUN3D dataset, we find that this approach produces registration results with greater accuracy and better robustness than previous alternatives.