Abstract

We present a robust approach for reconstructing the architectural structure of complex indoor environments given a set of cluttered input scans. Our method first uses an efficient occlusion-aware process to extract planar patches as potential wall segments, separating them from clutter and coping with missing data. Using a diffusion process to further increase its robustness, our algorithm is able to reconstruct a clean architectural model from those potential wall segments. To our knowledge, this is the first indoor reconstruction method which goes beyond a binary classification and auto- matically recognizes different rooms as separate components. We demonstrate the validity of our approach by testing it on both synthetic models and real-world 3d scans of indoor environments.