The Isprs Benchmark on Indoor Modelling

Khoshelham, Kourosh; Díaz-Vilariño, L.; Peter, Michael; Zhang, Kang; Acharya, Debaditya

doi:10.5194/isprs-archives-xlii-2-w7-367-2017

Cited by 76 publications

(61 citation statements)

References 16 publications

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“…Spatial models of indoor environments are needed in a growing number of applications including navigation, emergency response and a range of location-based services. Test data in this benchmark comprises five point clouds captured by different sensors in indoor environments of various complexities which should be processed to the model used for the purpose of indoor navigation (Khoshelham et al, 2017).…”

Section: Benchmark On Indoor Modellingmentioning

confidence: 99%

A Review of Benchmarking in Photogrammetry and Remote Sensing

Bakuła

Mills

Remondino

2019

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

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<p><strong>Abstract.</strong> This paper is a preface of the ISPRS/EuroSDR Workshop on the “Evaluation and Benchmarking of Sensors, Systems and Geospatial Data in Photogrammetry and Remote Sensing” that was held in Warsaw University of Technology on 16-17 September 2019. The paper reviews benchmarking in photogrammetry and remote sensing found in the literature relating to geodata. The first part of the paper is a bibliographic analysis based on queries in Scopus and Web of Science databases which shows an increase in research activities based on benchmarking data. In the second part, a review of past and ongoing benchmarking initiatives is presented, providing examples of initiatives within e.g. ISPRS and EuroSDR. The topic of evaluating data, sensors and algorithms with benchmarking activities is interesting as it provides the opportunity to compare, with a unique approach, research results from independent scientists. As hereafter reported, benchmarking has increased in recent years, with many benchmarks being presented in the photogrammetric and remote sensing communities.</p>

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Section: Benchmark On Indoor Modellingmentioning

confidence: 99%

A Review of Benchmarking in Photogrammetry and Remote Sensing

Bakuła

Mills

Remondino

2019

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

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“…Their performance can hardly be compared since it mostly depends on the level of clutter and occlusions of the input point cloud and on the geometric, topological, and semantic detail of the output model. From this requirement, the ISPRS Benchmark on Indoor Modelling proposes the creation of a common framework for the evaluation and comparison of indoor modelling methods but it is still under development [12].…”

Section: State-of-the-artmentioning

confidence: 99%

Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing

2018

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Despite the increasing demand of updated and detailed indoor models, indoor reconstruction from point clouds is still in an early stage in comparison with the reconstruction of outdoor scenes. Specific challenges are related to the complex building layouts and the high presence of elements such as pieces of furniture causing clutter and occlusions. This work proposes an automatic method for modelling Manhattan-World indoors acquired with a mobile laser scanner in the presence of highly occluded walls. The core of the methodology is the transformation of indoor reconstruction into a labelling problem of structural cells in a 2D floor plan. Assuming the prevalence of orthogonal intersections between walls, indoor completion is formulated as an energy minimization problem using graph cuts. Doors and windows are detected from occlusions by implementing a ray-tracing algorithm. The methodology is tested in a real case study. Except for one window partially covered by a curtain, all building elements were successfully reconstructed.

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“…This paper focuses on room and corridor segmentation, so clear definitions of rooms and corridors are required [12,23]. In the Oxford dictionary, a room is defined as a part or division of a building enclosed by walls, a floor, and a ceiling, and a corridor can be considered to be a special room.…”

Section: Overviewmentioning

confidence: 99%

“…Comprehensive segmentation contains two parts: story segmentation and room segmentation. The input data in this section were captured by an MLS device in a Zeb-Revo sensor [23].…”

Section: Overviewmentioning

confidence: 99%

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Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

Yang

et al. 2018

Remote Sensing

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Abstract:The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.

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The Isprs Benchmark on Indoor Modelling

Cited by 76 publications

References 16 publications

A Review of Benchmarking in Photogrammetry and Remote Sensing

A Review of Benchmarking in Photogrammetry and Remote Sensing

Indoor Building Reconstruction from Occluded Point Clouds Using Graph-Cut and Ray-Tracing

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

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