Watertight Planar Surface Meshing of Indoor Point-Clouds with Voxel Carving

Turner, Eric; Zakhor, Avideh

doi:10.1109/3dv.2013.14

Cited by 22 publications

(18 citation statements)

References 28 publications

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“…Specifically, our method omits interior clutter such as furniture since it uses wall samples as input. Figure 6 compares the models resulting from our 2.5D method with that of an existing 3D building modeling technique (Turner and Zakhor, 2013) for the hotel hallways shown in Figure 1. The two methods result in 2,944 triangles and 4.1 millions triangles, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…The room shown in purple in the lowerleft corner of this model also shows an example error in the building reconstruction. The adjacent room to the right was briefly seen through a window, but its area was considered part of this purple room rather than being removed in the manner described in Section 4.3, resulting in a small extrusion remaining in (Turner and Zakhor, 2013).…”

Section: Resultsmentioning

confidence: 99%

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Floor Plan Generation and Room Labeling of Indoor Environments from Laser Range Data

Turner

Zakhor

2014

Proceedings of the 9th International Conference on Computer Graphics Theory and Applications

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Abstract:Automatic generation of building floor plans is useful in many emerging applications, including indoor navigation, augmented and virtual reality, as well as building energy simulation software. These applications require watertight models with limited complexity. In this paper, we present an approach that produces 2.5D extruded watertight models of building interiors from either 2D particle filter grid maps or full 3D point-clouds captured by mobile mapping systems. Our approach is to triangulate a 2D sampling of wall positions and separate these triangles into interior and exterior sets. We partition the interior volume of the building model by rooms, then simplify the model to reduce noise. Such labels are useful for building energy simulations involving thermal models, as well as for ensuring geometric accuracy of the resulting 3D model. We experimentally verify the performance of our proposed approach on a wide variety of buildings. Our approach is efficient enough to be used in real-time in conjunction with Simultaneous Localization and Mapping (SLAM) applications.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Floor Plan Generation and Room Labeling of Indoor Environments from Laser Range Data

Turner

Zakhor

2014

Proceedings of the 9th International Conference on Computer Graphics Theory and Applications

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“…Delaunay triangulation [24] and its variant, the constrained Delaunay triangulation [25], are widely applied in 3D games or movies for surface reconstruction. To improve the efficiency and robustness to noise, quadtree-based triangulation has also been proposed [26,27] for planar surface reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

2016

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Abstract:The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC) in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme-the Incremental Recursive Segmentation (IRIS), that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data.

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“…Many methods exist that can convert a point cloud to a triangulated mesh. Using the method presented by Turner and Zakhor [42], we obtain a watertight mesh that represents the interior space of the environment. The meshing result of the point cloud from Figure 12a contains 4 million faces and takes up only 91MB on disk.…”

Section: Applicationsmentioning

confidence: 99%

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

Corso

Zakhor

2013

Remote Sensing

Self Cite

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Indoor localization and mapping is an important problem with many applications such as emergency response, architectural modeling, and historical preservation. In this paper, we develop an automatic, off-line pipeline for metrically accurate, GPS-denied, indoor 3D mobile mapping using a human-mounted backpack system consisting of a variety of sensors. There are three novel contributions in our proposed mapping approach. First, we present an algorithm which automatically detects loop closure constraints from an occupancy grid map. In doing so, we ensure that constraints are detected only in locations that are well conditioned for scan matching. Secondly, we address the problem of scan matching with poor initial condition by presenting an outlier-resistant, genetic scan matching algorithm that accurately matches scans despite a poor initial condition. Third, we present two metrics based on the amount and complexity of overlapping geometry in order to vet the estimated loop closure constraints. By doing so, we automatically prevent erroneous loop closures from degrading the accuracy of the reconstructed trajectory. The proposed algorithms are experimentally verified using both controlled and real-world data. The end-to-end system performance is evaluated using 100 surveyed control points in an office environment and obtains a mean accuracy of 10 cm. Experimental results are also shown on three additional datasets from real world environments including a 1500 meter trajectory in a warehouse sized retail shopping center.

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Watertight Planar Surface Meshing of Indoor Point-Clouds with Voxel Carving

Cited by 22 publications

References 28 publications

Floor Plan Generation and Room Labeling of Indoor Environments from Laser Range Data

Floor Plan Generation and Room Labeling of Indoor Environments from Laser Range Data

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

Indoor Localization Algorithms for an Ambulatory Human Operated 3D Mobile Mapping System

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