Towards automatic indoor reconstruction of cluttered building rooms from point clouds

Previtali, Mattia; Barazzetti, Luigi; Brumana, Raffaella; Scaioni, Marco

doi:10.5194/isprsannals-ii-5-281-2014

Cited by 56 publications

(39 citation statements)

References 32 publications

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“…Then, in order not to miss any structural details, an offset space from the ceiling height interval is defined, as illustrated in Figure 4, in order to add points within it into the rasterization. This is valid, because vertical walls are as high as the ceiling height, while most clutter and windows are rather less than the ceiling height [10]. The offset space from the floor is not considered, because clutter near the floor is more hindrance than help.…”

Section: Rasterization and Noise Filteringmentioning

confidence: 99%

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung

Stachniss

Kim

2017

IJGI

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Abstract:In this paper, we introduce an automatic room segmentation approach based on morphological processing. The inputs are registered point-clouds obtained from either a static laser scanner or a mobile scanning system, without any required prior information or initial labeling satisfying specific conditions. The proposed segmentation method's main concept, based on the assumption that each room is bound by vertical walls, is to project the 3D point cloud onto a 2D binary map and to close all openings (e.g., doorways) to other rooms. This is achieved by creating an initial segment map, skeletonizing the surrounding walls of each segment, and iteratively connecting the closest pixels between the skeletonized walls. By iterating this procedure for all initial segments, the algorithm produces a "watertight" floor map, on which each room can be segmented by a labeling process. Finally, the original 3D points are segmented according to their 2D locations as projected on the segment map. The novel features of our approach are: (1) its robustness against occlusions and clutter in point-cloud input; (2) high segmentation performance regardless of the number of rooms or architectural complexity; and (3) straight segmentation boundary generation, all of which were proved in experiments with various sets of real-world, synthetic, and publicly available data. Additionally, comparisons with the five popular existing methods through both qualitative and quantitative evaluations demonstrated the feasibility of the proposed approach.

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Section: Rasterization and Noise Filteringmentioning

confidence: 99%

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung

Stachniss

Kim

2017

IJGI

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“…However, their method can only extract the building outer walls and omit the inner walls. (Previtali et al, 2014) Figure 1. The Pipeline of Methodology: 1) preprocessing; 2) floorplan extraction; 3) door detection; 4) room segmentation; 5) output 3D modeling.…”

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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“…There have been some studies for automatic room detection and modeling from very dense point clouds [2,28]. In [2,28], wall boundaries are extracted from a dense point cloud acquired using static LiDAR and mapped onto a 2D floor plane to construct a cell complex. This enables the modeling of the room polyhedra.…”

Section: Related Workmentioning

confidence: 99%

“…They are widely used in many fields, such as civil engineering [1,2], autonomous driving and robotics. In these applications, automated processing of the point cloud, such as semantic extraction, object recognition and 3D reconstruction is usually performed to improve the efficiency of reconstruction and the modeling or accuracy of recognition.…”

Section: Introductionmentioning

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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Towards automatic indoor reconstruction of cluttered building rooms from point clouds

Cited by 56 publications

References 32 publications

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Automatic Indoor Building Reconstruction From Mobile Laser Scanning Data

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

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