Automatic reconstruction of fully volumetric 3D building models from oriented point clouds
TL;DR: A novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem.
Abstract: We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds.
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TL;DR: A complete workflow that allows to generate 3D models from point clouds of buildings and extract fine-grained indoor navigation networks from those models, to support advanced path planning for disaster management and navigation of different types of agents is introduced.
84 citations
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.
Abstract: Creating high-level structured 3D models of real-world indoor scenes from captured data is a fundamental task which has important applications in many fields. Given the complexity and variability of interior environments and the need to cope with noisy and partial captured data, many open research problems remain, despite the substantial progress made in the past decade. In this survey, we provide an up-to-date integrative view of the field, bridging complementary views coming from computer graphics and computer vision. After providing a characterization of input sources, we define the structure of output models and the priors exploited to bridge the gap between imperfect sources and desired output. We then identify and discuss the main components of a structured reconstruction pipeline, and review how they are combined in scalable solutions working at the building level. We finally point out relevant research issues and analyze research trends.
76 citations
TL;DR: This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects through geometric primitive as well as relationship-based reasoning.
Abstract: This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.
58 citations
12 Oct 2015
TL;DR: This work presents a new method for automatic semantic structuring of 3D point clouds representing buildings that focuses on the building's interior using indoor scans to derive high-level architectural entities like rooms and doors.
Abstract: We present a new method for automatic semantic structuring of 3D point clouds representing buildings. In contrast to existing approaches which either target the outside appearance like the facade structure or rather low-level geometric structures, we focus on the building's interior using indoor scans to derive high-level architectural entities like rooms and doors. Starting with a registered 3D point cloud, we probabilistically model the affiliation of each measured point to a certain room in the building. We solve the resulting clustering problem using an iterative algorithm that relies on the estimated visibilities between any two locations within the point cloud. With the segmentation into rooms at hand, we subsequently determine the locations and extents of doors between adjacent rooms. In our experiments, we demonstrate the feasibility of our method by applying it to synthetic as well as to real-world data.
49 citations
TL;DR: A novel method is presented to automatically reconstruct BIM wall objects and their topology and the ability to reconstruct different wall axis and connection types and the simultaneous processing of entire multi-story structures is demonstrated.
40 citations
References
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TL;DR: This work presents two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves that allow important cases of discontinuity preserving energies.
Abstract: Many tasks in computer vision involve assigning a label (such as disparity) to every pixel. A common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist, e.g., at object boundaries. These tasks are naturally stated in terms of energy minimization. The authors consider a wide class of energies with various smoothness constraints. Global minimization of these energy functions is NP-hard even in the simplest discontinuity-preserving case. Therefore, our focus is on efficient approximation algorithms. We present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves. These moves can simultaneously change the labels of arbitrarily large sets of pixels. In contrast, many standard algorithms (including simulated annealing) use small moves where only one pixel changes its label at a time. Our expansion algorithm finds a labeling within a known factor of the global minimum, while our swap algorithm handles more general energy functions. Both of these algorithms allow important cases of discontinuity preserving energies. We experimentally demonstrate the effectiveness of our approach for image restoration, stereo and motion. On real data with ground truth, we achieve 98 percent accuracy.
7,413 citations
09 May 2011
TL;DR: PCL (Point Cloud Library) is presented, an advanced and extensive approach to the subject of 3D perception that contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation.
Abstract: With the advent of new, low-cost 3D sensing hardware such as the Kinect, and continued efforts in advanced point cloud processing, 3D perception gains more and more importance in robotics, as well as other fields. In this paper we present one of our most recent initiatives in the areas of point cloud perception: PCL (Point Cloud Library - http://pointclouds.org). PCL presents an advanced and extensive approach to the subject of 3D perception, and it's meant to provide support for all the common 3D building blocks that applications need. The library contains state-of-the art algorithms for: filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. PCL is supported by an international community of robotics and perception researchers. We provide a brief walkthrough of PCL including its algorithmic capabilities and implementation strategies.
4,501 citations
"Automatic reconstruction of fully v..." refers methods in this paper
...ing. For automatic labeling, MCL was used with default parameters (inflation set to 2.0) in multi-threaded mode. The surface cost weight in Equation 7 was empirically chosen as 0.04. We used PCL 1.8.1 [45], CGAL 4.12 [42, 46], MCL 14-137 [41], Gurobi 8.0.1 [44], and NVIDIA OptiX 5.0 for GPU-based ray casting under Linux on a 6-core Intel i7 CPU and a NVIDIA GeForce GTX 980 GPU. 6. Evaluation We evaluat...
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01 Jan 1999
TL;DR: This paper proposes two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed, and generates a labeling such that there is no expansion move that decreases the energy.
Abstract: In this paper we address the problem of minimizing a large class of energy functions that occur in early vision. The major restriction is that the energy function's smoothness term must only involve pairs of pixels. We propose two algorithms that use graph cuts to compute a local minimum even when very large moves are allowed. The first move we consider is an /spl alpha/-/spl beta/-swap: for a pair of labels /spl alpha/,/spl beta/, this move exchanges the labels between an arbitrary set of pixels labeled a and another arbitrary set labeled /spl beta/. Our first algorithm generates a labeling such that there is no swap move that decreases the energy. The second move we consider is an /spl alpha/-expansion: for a label a, this move assigns an arbitrary set of pixels the label /spl alpha/. Our second algorithm, which requires the smoothness term to be a metric, generates a labeling such that there is no expansion move that decreases the energy. Moreover, this solution is within a known factor of the global minimum. We experimentally demonstrate the effectiveness of our approach on image restoration, stereo and motion.
3,199 citations
"Automatic reconstruction of fully v..." refers background in this paper
...Approximate multi-label methods based on e.g. Graph Cuts [43] are more restricted regarding the family of objective functions and constraints that can be used and may fail to find good solutions if the objective is not sufficiently smooth....
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...Graph Cuts [43] are more restricted regarding the family of objective functions and constraints that can be used and may fail to find good solutions if the objective is not sufficiently smooth....
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TL;DR: An automatic algorithm to detect basic shapes in unorganized point clouds based on random sampling and detects planes, spheres, cylinders, cones and tori, and obtains a representation solely consisting of shape proxies.
Abstract: In this paper we present an automatic algorithm to detect basic shapes in unorganized point clouds. The algorithm decomposes the point cloud into a concise, hybrid structure of inherent shapes and a set of remaining points. Each detected shape serves as a proxy for a set of corresponding points. Our method is based on random sampling and detects planes, spheres, cylinders, cones and tori. For models with surfaces composed of these basic shapes only, for example, CAD models, we automatically obtain a representation solely consisting of shape proxies. We demonstrate that the algorithm is robust even in the presence of many outliers and a high degree of noise. The proposed method scales well with respect to the size of the input point cloud and the number and size of the shapes within the data. Even point sets with several millions of samples are robustly decomposed within less than a minute. Moreover, the algorithm is conceptually simple and easy to implement. Application areas include measurement of physical parameters, scan registration, surface compression, hybrid rendering, shape classification, meshing, simplification, approximation and reverse engineering.
1,800 citations
"Automatic reconstruction of fully v..." refers methods in this paper
...To this end, an efficient RANSAC approach [40] implemented in CGAL [42] is used....
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...We first detect planes using an efficient RANSAC implementation [40] (Figure 1 b) and compute occupancy bitmaps for each detected plane from the respective supporting points....
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Posted Content•
TL;DR: A dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations, enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large- scale indoor spaces.
Abstract: We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000m2 and contains over 70,000 RGB images, along with the corresponding depths, surface normals, semantic annotations, global XYZ images (all in forms of both regular and 360° equirectangular images) as well as camera information. It also includes registered raw and semantically annotated 3D meshes and point clouds. The dataset enables development of joint and cross-modal learning models and potentially unsupervised approaches utilizing the regularities present in large-scale indoor spaces. The dataset is available here: this http URL
653 citations
"Automatic reconstruction of fully v..." refers methods in this paper
...We also used datasets provided by The Royal Danish Academy of Fine Arts Schools of Architecture, Design and Conservation (CITA) (Table 2), from The ISPRS Benchmark on Indoor Modeling [47] (Figure 10), and from the Stanford 3D Large-Scale Indoor Spaces Dataset [48] (Figure 9)....
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...Figure 5 shows the dataset “synth3” by the Visualization and MultiMedia Lab at University of Zurich, Figure 10 depicts the dataset “Case study 2” from the ISPRS Benchmark on Indoor Modeling [47], and Figure 9 shows the dataset “Area 3” from the Stanford 3D Large-Scale Indoor Spaces Dataset [48]....
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...Large-Scale Indoor Spaces Dataset [48] (Figure 9)....
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