3D Reconstruction of Indoor Corridor Models Using Single Imagery and Video Sequences

In recent years, 3D indoor modeling has gained more attention due to its role in decision-making process of maintaining the status and managing the security of building indoor spaces. In this thesis, the problem of continuous indoor corridor space modeling has been tackled through two approaches. The first approach develops a modeling method based on middle-level perceptual organization. The second approach develops a visual Simultaneous Localisation and Mapping (SLAM) system with model-based loop closure. In the first approach, the image space was searched for a corridor layout that can be converted into a geometrically accurate 3D model. Manhattan rule assumption was adopted, and indoor corridor layout hypotheses were generated through a random rule-based intersection of image physical line segments and virtual rays of orthogonal vanishing points. Volumetric reasoning, correspondences to physical edges, orientation map and geometric context of an image are all considered for scoring layout hypotheses. This approach provides physically plausible solutions while facing objects or occlusions in a corridor scene. In the second approach, Layout SLAM is introduced. Layout SLAM performs camera localization while maps layout corners and normal point features in 3D space. Here, a new feature matching cost function was proposed considering both local and global context information. In addition, a rotation compensation variable makes Layout SLAM robust against cameras orientation errors accumulations. Moreover, layout model matching of keyframes insures accurate loop closures that prevent miss-association of newly visited landmarks to previously visited scene parts. The comparison of generated single image-based 3D models to ground truth models showed that average ratio differences in widths, heights and lengths were 1.8%, 3.7% and 19.2% respectively. Moreover, Layout SLAM performed with the maximum absolute trajectory error of 2.4m in position and 8.2 degree in orientation for approximately 318m path on RAWSEEDS data set. Loop closing was strongly performed for Layout SLAM and provided 3D indoor corridor layouts with less than 1.05m displacement errors in length and less than 20cm in width and height for approximately 315m path on York University data set. The proposed methods can successfully generate 3D indoor corridor models compared to their major counterpart

Automatic reconstruction of parametric building models from indoor point clouds

AbstractWe present an automatic approach for the reconstruction of parametric 3D building models from indoor point clouds. While recently developed methods in this domain focus on mere local surface reconstructions which enable e.g. efficient visualization, our approach aims for a volumetric, parametric building model that additionally incorporates contextual information such as global wall connectivity. In contrast to pure surface reconstructions, our representation thereby allows more comprehensive use: first, it enables efficient high-level editing operations in terms of e.g. wall removal or room reshaping which always result in a topologically consistent representation. Second, it enables easy taking of measurements like e.g. determining wall thickness or room areas. These properties render our reconstruction method especially beneficial to architects or engineers for planning renovation or retrofitting. Following the idea of previous approaches, the reconstruction task is cast as a labeling problem which is solved by an energy minimization. This global optimization approach allows for the reconstruction of wall elements shared between rooms while simultaneously maintaining plausible connectivity between all wall elements. An automatic prior segmentation of the point clouds into rooms and outside area filters large-scale outliers and yields priors for the definition of labeling costs for the energy minimization. The reconstructed model is further enriched by detected doors and windows. We demonstrate the applicability and reconstruction power of our new approach on a variety of complex real-world datasets requiring little or no parameter adjustment

Advances in Data-Driven Analysis and Synthesis of 3D Indoor Scenes

This report surveys advances in deep learning-based modeling techniques that address four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We describe different kinds of representations for indoor scenes, various indoor scene datasets available for research in the aforementioned areas, and discuss notable works employing machine learning models for such scene modeling tasks based on these representations. Specifically, we focus on the analysis and synthesis of 3D indoor scenes. With respect to analysis, we focus on four basic scene understanding tasks -- 3D object detection, 3D scene segmentation, 3D scene reconstruction and 3D scene similarity. And for synthesis, we mainly discuss neural scene synthesis works, though also highlighting model-driven methods that allow for human-centric, progressive scene synthesis. We identify the challenges involved in modeling scenes for these tasks and the kind of machinery that needs to be developed to adapt to the data representation, and the task setting in general. For each of these tasks, we provide a comprehensive summary of the state-of-the-art works across different axes such as the choice of data representation, backbone, evaluation metric, input, output, etc., providing an organized review of the literature. Towards the end, we discuss some interesting research directions that have the potential to make a direct impact on the way users interact and engage with these virtual scene models, making them an integral part of the metaverse.Comment: Published in Computer Graphics Forum, Aug 202

Mapping beyond what you can see: Predicting the layout of rooms behind closed doors

The availability of maps of indoor environments is often fundamental for autonomous mobile robots to efficiently operate in industrial, office, and domestic applications. When robots build such maps, some areas of interest could be inaccessible, for instance, due to closed doors. As a consequence, these areas are not represented in the maps, possibly causing limitations in robot localization and navigation. In this paper, we provide a method that completes 2D grid maps by adding the predicted layout of the rooms behind closed doors. The main idea of our approach is to exploit the underlying geometrical structure of indoor environments to estimate the shape of unobserved rooms. Results show that our method is accurate in completing maps also when large portions of environments cannot be accessed by the robot during map building. We experimentally validate the quality of the completed maps by using them to perform path planning tasks.(c) 2022 Elsevier B.V. All rights reserved

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

Recent years have seen a proliferation of new digital products for the efficient management of indoor spaces, with important applications like emergency management, virtual property showcasing and interior design. These products rely on accurate 3D models of the environments considered, including information on both architectural and non-permanent elements. These models must be created from measured data such as RGB-D images or 3D point clouds, whose capture and consolidation involves lengthy data workflows. This strongly limits the rate at which 3D models can be produced, preventing the adoption of many digital services for indoor space management. We provide an alternative to such data-intensive procedures by presenting Walk2Map, a data-driven approach to generate floor plans only from trajectories of a person walking inside the rooms. Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces. Our work is based on learning the latent relation between an indoor walk trajectory and the information represented in a floor plan: interior space footprint, portals, and furniture. We distinguish between recovering area-related (interior footprint, furniture) and wall-related (doors) information and use two different neural architectures for the two tasks: an image-based Encoder-Decoder and a Graph Convolutional Network, respectively. We train our networks using scanned 3D indoor models and apply them in a cascaded fashion on an indoor walk trajectory at inference time. We perform a qualitative and quantitative evaluation using both simulated and measured, real-world trajectories, and compare against a baseline method for image-to-image translation. The experiments confirm the feasibility of our approach.Comment: To be published in Computer Graphics Forum (Proc. Eurographics 2021