Enhanced Reconstruction of Architectural Wall Surfaces for 3D Building Models

The reconstruction of architectural structures from 3D building models is a challenging task and a lot of research has been done in recent years. However, most of this work is focused mainly on reconstructing accurately the architectural shape of interiors rather than the fine architectural details, such as the wall elements (e.g. windows and doors). We focus specifically on this problem and propose a method that extends current solutions to reconstruct accurately severely occluded wall surfaces

ASPIRE: Automatic scanner position reconstruction

The recent advances in 3D laser range scanning have led to significant improvements in capturing and modeling 3D envi- ronments, allowing the creation of highly expressive and semantically rich 3D models from indoor environments, generally known as building information models. Despite the capabilities of state-of-the-art methods to generate faithful architectural 3D building models, the majority of them rely explicitly on the prior knowledge of scanner positions in order to reconstruct them successfully. However, in real-world applications, this metadata information gets typically lost after the point cloud registration, which means that none of these methods could work in practice and the creation of their building models would be impossible. Therefore, we present a novel pipeline that allows to automatically and accurately reconstruct the original scanner positions under very challenging conditions, without requiring any prior knowledge about the environment or the dataset. Being independent from laser range scanner manufacturers, it can be applied to almost every real-world LiDAR appli- cation. Our method exploits only information derived from the raw point data and is applicable to all scientific and industrial applications, where the original scan positions typically get lost after registration by the proprietary software provided by the scanner manufacturers. We demonstrate the validity of our approach by evaluating it on several real-world and synthetic indoor environments

Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments

In this paper, a new method capable to extract the wall openings (windows and doors) of interior scenes from point clouds under cluttered and occluded environments is presented. For each wall surface extracted by the polyhedral model of a room, our method constructs a cell complex representation, which is used for the wall object segmentation using a graph cut method. We evaluate the results of the proposed approach on real-world 3D scans of indoor environments and demonstrate its validity

Automatic reconstruction of wall features under clutter and occlusion

In this paper, a new method to extract wall openings (windows and doors) in interior scenes from point clouds under cluttered and occluded environments is presented. For each wall surface or a room represented by a bounding polyhedron and its 3D scan points, our method constructs a planar cell complex representation, which is used for the wall features segmentation using a graph-cut method. We evaluate our results of the proposed approach on real-world 3D scans of indoor environments and demonstrate its validity

Refinement and optimization methods for reconstructing and modeling indoor environments

Driven by the development in digital 3D imaging and scanning technology, the efficient capturing and modeling of 3D objects and environments has become a critical task in many application domains such as in architecture, engineering, robotics, navigation, construction and facility management. In particular, the availability of highly expressive 3D models from indoor environments could create highly added value in these domains, since these models, generally known as Building Information Models (BIMs), could provide semantically rich representations of the scene, allowing its analysis and manipulation. Also, they could provide accurate identification of their main architectural wall structures, such as the windows and doors, based on their as-is condition, which might be significantly different from the one they were designed. Despite the many recent research efforts, the fully automatic generation of semantically enriched 3D models from building interiors still remains a very challenging and time-consuming process. The main difficulties lie on the accurate and fast acquisition of the surrounding environment and the creation of a faithful and semantically rich 3D model. Although the open problems in the field are still manifold, we focused in this thesis specifically on the following three important challenges: first, how to capture efficiently the 3D information of the scene, using a method which combines increased accuracy, high performance and produces adequate results for the majority of real-world indoor applications; second, how to faithfully capture the finer details of structural building elements and generate a semantically rich building model; third, how to allow the successful and automatic reconstruction of 3D BIM models without relying on restrictive assumptions about the scene or the dataset. For each of these issues, we propose a solution which advances the state-of- the-art and allows for improved performance and better quality to the extracted results. Our first work focuses on the efficient and fast acquisition of the scene and introduces a new hardware-efficient stereo vision method. In our method, a local-based correlation algorithm computes the matching cost values and an optimization technique based on Discrete Dynamical Systems refines the extracted depth information. In the same contribution, we propose also an efficient parallel-pipelined hardware architecture, which implements the proposed stereo reconstruction method on a custom FPGA device, allowing high processing speeds for high-resolution stereo images. The second research contribution of this thesis focuses on the 3D modeling stage of the reconstruction pipeline and aims to recover and semantically label the architectural wall elements of indoor environments. This is achieved by partitioning the wall surfaces of the reconstructed building models into small planar patches and classifying them using a bayesian graph-cut optimization technique. Due to its beneficial design, our approach can be embedded as a post-processing unit to the majority of modern modeling pipelines, enabling them to extract automatically semantically rich 3D models. The last research contribution of this thesis lifts a restrictive and widely-used assumption in the field, that the scanner viewpoint positions should be known a priori, in order the reconstruction and 3D modeling of the scene to be performed successfully. Specifically, this work constitutes the first method for re-engineering and reconstructing the original scanner positions from raw point clouds. It relies on the scanning characteristics of the acquisition process and employs a statistical analysis to raw point data, in order to reveal the features which will allow the retrieval of the true scanner positions. Extensive qualitative and quantitative evaluations on real-world and synthetic datasets reveal the advantageous behavior of the proposed methods, as well as their efficiency and performance under challenging and difficult indoor conditions

High performance stereo system for dense 3D reconstruction

3-D stereo reconstruction, a technique that estimates per-pixel depth in a scene, is still a challenging problem mainly due to some prohibitive factors that limit its performance and computational ability. The aim of this paper is to present a new hardware-efficient disparity map computation, which is based on disparity space image processing using discrete dynamic systems. The hardware architecture of the proposed system was implemented on a high-end field programmable gate array (FPGA) device, offering real-time 3-D reconstruction speeds using a hardware aware architecture based on parallelism and process pipelining. The proposed architecture fulfills the requirements of real-world applications regarding resource usage, frame rates, and disparity resolution, while its implementation on an Altera Stratix IV family FPGA device can extract disparity maps of up to 1280 × 1024 pixels with up to 128 disparity levels under real-time or near real-time conditions at a clock rate of 168 MHz. Qualitative and quantitative results also demonstrate its performance and improvement over previous hardware-related studies, making our approach a suitable candidate for applications in which timing and processing constraints are critical