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

Oesau, Sven; Lafarge, Florent; Alliez, Pierre

doi:10.1016/j.isprsjprs.2014.02.004

Cited by 202 publications

(210 citation statements)

References 20 publications

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“…In practice, most existing methods reconstruct geometric elements only (Becker et al, 2015;Díaz-Vilariño et al, 2015;Mura et al, 2016;Oesau et al, 2014;Sanchez and Zakhor, 2012;Thomson and Boehm, 2015;Valero et al, 2012;Xiao and Furukawa, 2012;Xiong et al, 2013), while a few can model spaces and the topological relations between them (Khoshelham and Díaz-Vilariño, 2014;Tran et al, 2017). Nonetheless, a comprehensive evaluation should take into account all three components of an indoor model.…”

Section: Evaluation Criteriamentioning

confidence: 99%

The Isprs Benchmark on Indoor Modelling

Khoshelham

Díaz-Vilariño

Peter

et al. 2017

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

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ABSTRACT:Automated generation of 3D indoor models from point cloud data has been a topic of intensive research in recent years. While results on various datasets have been reported in literature, a comparison of the performance of different methods has not been possible due to the lack of benchmark datasets and a common evaluation framework. The ISPRS benchmark on indoor modelling aims to address this issue by providing a public benchmark dataset and an evaluation framework for performance comparison of indoor modelling methods. In this paper, we present the benchmark dataset comprising several point clouds of indoor environments captured by different sensors. We also discuss the evaluation and comparison of indoor modelling methods based on manually created reference models and appropriate quality evaluation criteria. The benchmark dataset is available for download at:

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Section: Evaluation Criteriamentioning

confidence: 99%

The Isprs Benchmark on Indoor Modelling

Khoshelham

Díaz-Vilariño

Peter

et al. 2017

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

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“…Their method works well for stationary terrestrial scanning data, but not for the data from indoor mobile LiDAR system. (Oesau et al, 2014) present a two-line multi-scale fitting method to label the rooms through an energy function optimization based on graph cut and the line-of-sight information from each point to the laser scanner. However, their method can only extract the building outer walls and omit the inner walls.…”

Section: Related Workmentioning

confidence: 99%

Automatic Indoor Building Reconstruction From Mobile Laser Scanning Data

Xie

Wang

2017

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

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ABSTRACT:Indoor reconstruction from point clouds is a hot topic in photogrammetry, computer vision and computer graphics. Reconstructing indoor scene from point clouds is challenging due to complex room floorplan and line-of-sight occlusions. Most of existing methods deal with stationary terrestrial laser scanning point clouds or RGB-D point clouds. In this paper, we propose an automatic method for reconstructing indoor 3D building models from mobile laser scanning point clouds. The method includes 2D floorplan generation, 3D building modeling, door detection and room segmentation. The main idea behind our approach is to separate wall structure into two different types as the inner wall and the outer wall based on the observation of point distribution. Then we utilize a graph cut based optimization method to solve the labeling problem and generate the 2D floorplan based on the optimization result. Subsequently, we leverage an α-shape based method to detect the doors on the 2D projected point clouds and utilize the floorplan to segment the individual room. The experiments show that this door detection method can achieve a recognition rate at 97% and the room segmentation method can attain the correct segmentation results. We also evaluate the reconstruction accuracy on the synthetic data, which indicates the accuracy of our method is comparable to the state-of-the art.

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“…Space partitioning is a necessary step for surface reconstruction via graph cuts (Chauve et al, 2010;Oesau et al, 2014). It can be accomplished via various approaches, such as voxel rasterization (Chehata et al, 2009;Paris et al, 2006), 3D Delaunay triangulation (Labatut et al, 2007;Labatut et al, 2009) and space subdivision by hyperplanes (Chauve et al, 2010;Oesau et al, 2014).…”

Section: Space Partitioningmentioning

confidence: 99%

“…It can be accomplished via various approaches, such as voxel rasterization (Chehata et al, 2009;Paris et al, 2006), 3D Delaunay triangulation (Labatut et al, 2007;Labatut et al, 2009) and space subdivision by hyperplanes (Chauve et al, 2010;Oesau et al, 2014). Note that the surfaces of the reconstructed building are the subset of the volumetric surfaces derived from space partitioning.…”

Section: Space Partitioningmentioning

confidence: 99%

A Global Solution to Topological Reconstruction of Building Roof Models From Airborne Lidar Point Clouds

Yan

Jiang

Shan

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global. Roof faces and edges as well as their topology can be determined at one time via minimization of an energy function. Besides, this approach is robust to partial absence of roof planes and tends to reconstruct roof models with visibility-consistent surfaces.

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

Cited by 202 publications

References 20 publications

The Isprs Benchmark on Indoor Modelling

The Isprs Benchmark on Indoor Modelling

Automatic Indoor Building Reconstruction From Mobile Laser Scanning Data

A Global Solution to Topological Reconstruction of Building Roof Models From Airborne Lidar Point Clouds

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