Robust Reconstruction of Interior Building Structures with Multiple Rooms under Clutter and Occlusions

Abstract

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 candidate walls, 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 the candidate walls. To our knowledge, this is the first indoor reconstruction method which goes beyond a binary classification and automatically 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.

Building an IT platform for strategic crisis management preparation

“…For instance, visibility reasoning is more problematic since a floor plan may contain several interconnected rooms, in addition interiors are often dominated by structures that lack visual and geometric details to perform an automatic reconstruction. For example standard approaches [17], [18] produce high resolution 3D models, but are strongly time comsuming and basically an overkill for a large branch of applications. As highlighted by the recent presentation of Google Project Tango [19], the use of mobile devices to create a 3D map of an indoor environment is instead a growing and promising approach, especially for applications focused on the structure of a building rather than the details of the model.…”

Semantic Segmentation of Indoor Point Clouds Using Convolutional Neural Network

“…Various methods have recently been developed based on projection plane histogram analysis (Okorn et al, 2010), plane sweep (Budroni and Boehm, 2010), surface normal (Sanchez and Zakhor, 2012), stacking (Xiong et al, 2013), and diffusion embedding (Mura et al, 2013). Common to all these methods is the sequential approach to label the structural elements according to previously segmented planar surfaces.…”

Modelling of Building Interiors with Mobile Phone Sensor Data

“…While advances in laser scanning (e.g., Mura et al [1] and Oesau et al [2]), low-cost depth cameras (e.g., Henry et al [3] and Khoshelham and Elberink [4]) and imagery-based photogrammetric methods (e.g., Colburn et al [5] and Cabral and Furukawa [6]) are making indoor 3D reconstruction more accessible, such methods often require expensive equipment, trained operators and further detailed and skilled modelling steps to generate useful information from large point cloud datasets. For these reasons, and given the prevalence of smartphones, our experiments and system design focus on using imprecise measurements of interior spaces estimated from mobile phone orientation sensors.…”