3D Reconstruction of Interior Wall Surfaces under Occlusion and Clutter

Adán, Antonio; Huber, Daniel

doi:10.1109/3dimpvt.2011.42

Cited by 153 publications

(125 citation statements)

References 15 publications

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“…Following that and in order to filter the point clouds corresponding to the floor and the ceiling, a rough segmentation of the point cloud was performed by considering the histogram of the z-coordinates, as is done in Adán et al [15]. The two maximum values of the histogram correspond to the elevation of ceiling and floor ( Figure 2).…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

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Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos

Díaz-Vilariño

Balado

et al. 2018

IJGI

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This paper presents a methodology for the automatic selection of heuristic scanning positions in unknown indoor environments. The surveying is carried out by a robotic system following a stop-and-go procedure. Starting with a random scan position in the room, the point cloud is discretized in voxels and they are submitted to a two-step classification and are labelled as occupied, occluded, empty, window, door, or exterior based on a visibility analysis. The main objective of the methodology is to obtain a complete point cloud of the indoor space and accordingly, the next best position is the scan position minimizing occluded voxels. Because the method locates doors and windows, the room can be delimited and the scan can continue for adjacent rooms. This approach has been tested in a real case study, in which three scans were developed.

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Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…To achieve correct modelling from the 3D information of an interior environment [12][13][14][15][16][17] the occluded space must be minimized.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

González-deSantos

Díaz-Vilariño

Balado

et al. 2018

IJGI

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“…However, neither structural relations nor volumes of the indoor space are provided. Adan and Huber (2011) build on top of this approach and focus on the modeling of wall surfaces. A learning mechanism is proposed for segmenting and labeling wall surfaces into parts either occluded by clutter, or occupied (solid), or empty (e.g.…”

Section: Related Workmentioning

confidence: 99%

Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut

Oesau

Lafarge

Alliez

2014

ISPRS Journal of Photogrammetry and Remote Sensing

200

203

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We present a method for automatic reconstruction of permanent structures, such as walls, floors and ceilings, given a raw point cloud of an indoor scene. The main idea behind our approach is a graph-cut formulation to solve an inside/outside labeling of a space partitioning. We first partition the space in order to align the reconstructed models with permanent structures. The horizontal structures are located through analysis of the vertical point distribution, while vertical wall structures are detected through feature preserving multi-scale line fitting, followed by clustering in a Hough transform space. The final surface is extracted through a graph-cut formulation that trades faithfulness to measurement data for geometric complexity. A series of experiments show watertight surface meshes reconstructed from point clouds measured on multi-level buildings.

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“…This content can be grouped into three geometrically significant clusters with respect to surface normal distributions as: the horizontal structures, vertical structures, and clutter which might be existent as dissolved in any direction, anisotropically-heterogeneously, throughout the whole point cloud. The common approach to leave out walls in this mixed data, is to start with extracting ceiling and floor within horizontal structures, and further eliminate clutter from the remaining cloud (Oesau et al, 2014), (Mura et al, 2013), (Adan and Huber, 2011), (Okorn et al, 2010). For this purpose, the inherent density predominance of ceiling and floor is exploited in a point density histogram along the vertical axis, and clutter is removed by estimating surface normal, and filtering points whose normal are not parallel in the vertical direction.…”

Section: Volumetric Slicingmentioning

confidence: 99%

Towards Object Driven Floor Plan Extraction From Laser Point Cloud

Babacan¹,

Jung²,

Wichmann³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:During the last years, the demand for indoor models has increased for various purposes. As a provisional step to proceed towards higher dimensional indoor models, powerful and flexible floor plans can be utilised. Therefore, several methods have been proposed that provide automatically generated floor plans from laser point clouds. The prevailing methodology seeks to attain semantic enhancement of a model (e.g. the identification and labelling of its components) built upon already reconstructed (a priori) geometry. In contrast, this paper demonstrates preliminary research on the possibility to directly incorporate semantic knowledge, which is itself derived from the raw data during the extraction, into the geometric modelling process. In this regard, we propose a new method to automatically extract floor plans from raw point clouds. It is based on a hierarchical space partitioning of the data, integrated with primitive selection actuated by object detection. First, planar primitives corresponding to vertical architectural structures are extracted using M-estimator SAmple and Consensus (MSAC). The set of the resulting line segments are refined by a selection process through a novel door detection algorithm, considering optimization of prior information and fitness to the data. The selected lines are used as hyperlines to partition the space into enclosed areas. Finally, a floor plan is extracted from these partitions by Minimum Description Length (MDL) hypothesis ranking. The algorithm is applied on a real mobile laser scanner dataset and the results are evaluated both in terms of door detection and consecutive floor plan extraction.

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3D Reconstruction of Interior Wall Surfaces under Occlusion and Clutter

Cited by 153 publications

References 15 publications

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

Autonomous Point Cloud Acquisition of Unknown Indoor Scenes

Indoor scene reconstruction using feature sensitive primitive extraction and graph-cut

Towards Object Driven Floor Plan Extraction From Laser Point Cloud

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