Piecewise Planar and Compact Floorplan Reconstruction from Images

Abstract

This paper presents a system to reconstruct piecewise planar and compact floorplans from images, which are then converted to high quality texture-mapped models for freeviewpoint visualization. There are two main challenges in image-based floorplan reconstruction. The first is the lack of 3D information that can be extracted from images by Structure from Motion and Multi-View Stereo, as indoor scenes abound with non-diffuse and homogeneous surfaces plus clutter. The second challenge is the need of a sophisticated regularization technique that enforces piecewise planarity, to suppress clutter and yield high quality texture mapped models. Our technical contributions are twofold. First, we propose a novel structure classification technique to classify each pixel to three regions (floor, ceiling, and wall), which provide 3D cues even from a single image. Second, we cast floorplan reconstruction as a shortest path problem on a specially crafted graph, which enables us to enforce piecewise planarity. Besides producing compact piecewise planar models, this formulation allows us to directly control the number of vertices (i.e., density) of the output mesh. We evaluate our system on real indoor scenes, and show that our texture mapped mesh models provide compelling free-viewpoint visualization experiences, when compared against the state-of-the-art and ground truth.

Room layout estimation from rapid omnidirectional exploration

“…Probably the conceptually closest approach to ours is the one by Cabral and Furukawa [3]. It also employs wide field of view cameras and 3D reconstruction.…”

Towards Object Driven Floor Plan Extraction From Laser Point Cloud

“…Image based reconstructions (Cabral and Furukawa, 2013), or stereo-vision systems providing point clouds (Henry et al, 2012) have apparent limitations with respect to precision demanding projects. Potentially, laser scanners provide immense amount of data appropriate for this endeavour.…”

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.…”