Claudio Mura

TL;DR: A robust approach for reconstructing the main architectural structure of complex indoor environments given a set of cluttered 3D input range scans, using an efficient occlusion-aware process and automatically extracts the individual rooms that compose the environment by applying a diffusion process on the space partitioning induced by the candidate walls.

TL;DR: A trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly‐planar patches related by geometric transformations, enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of the method.

TL;DR: A novel pipeline is presented that allows to reconstruct general 3D interior architectures, significantly increasing the range of real‐world architectures that can be reconstructed and labeled by any interior reconstruction method to date.

TL;DR: An up‐to‐date integrative view of the field, bridging complementary views coming from computer graphics and computer vision is provided, and the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output are defined.

TL;DR: This work presents the first indoor reconstruction method which goes beyond a binary classification and automatically recognizes different rooms as separate components and reconstructs a clean architectural model from the candidate walls.