Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts

Mura, Claudio; Mattausch, Oliver; Villanueva, Alberto Jaspe; Gobbetti, Enrico; Pajarola, Renato

doi:10.1016/j.cag.2014.07.005

Cited by 160 publications

(159 citation statements)

References 27 publications

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“…Some methods, for example Macher et al [18] and Ikehata et al [31], do not require known scanner locations or prior knowledge, but operate only under specific conditions. Further, some methods are limited to segmentation of rooms with planar walls [15,30,31], which fact narrows the range of their practical applications. Table 1 provides a summary of the room segmentation methods used in the robotics and AEC communities as well as a comparison with our approach.…”

Section: Discussionmentioning

confidence: 99%

“…Mura et al [15] propose an iterative binary subdivision algorithm. It starts by projecting the entire point cloud onto the x-y plane.…”

Section: Room Segmentation In Aec Communitymentioning

confidence: 99%

“…Except for parameter selection, our approach is automatic and involves no manual intervention. The main assumptions, which hold valid for typical indoor environments, are: (1) ceilings and floors are planar and parallel structures bound by vertical walls; and (2) different rooms and corridors are connected by narrow passages such as doorways [7][8][9]15]. phases (2) to (6), respectively.…”

Section: Overviewmentioning

confidence: 99%

See 2 more Smart Citations

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: Discussionmentioning

confidence: 99%

“…Mura et al [15] propose an iterative binary subdivision algorithm. It starts by projecting the entire point cloud onto the x-y plane.…”

Section: Room Segmentation In Aec Communitymentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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Automatic Room Segmentation of 3D Laser Data Using Morphological Processing

Jung

Stachniss

Kim

2017

IJGI

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“…However, their method is based on the Manhattan world assumption that restricts the types of geometric expressions. Another approach proposed by (Mura et al, 2014) explores the vertical structure (i.e. planar patches from the walls) and a visibility check to eliminate occlusions through the position of static laser scanner.…”

Section: Stationary Terrestrial Laser Scanningmentioning

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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“…The recent solution in that domain requires 3D scanning of objects inside the room provided that the distance of the objects from the 3D scanner is at a known fixed position [3]. The solution for this problem is not constrained by execution time.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of a 3D Virtual Colon Structure and Camera Motion for Screening Colonoscopy

Tavanapong

Hong

Wong

et al. 2017

MRAJ

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Background/PurposeOptical colonoscopy is the gold standard for detection and prevention of Colorectal cancer. However, any lesion outside the field of view of the camera during colonoscopy is missed, and can develop from a polyp into a cancer or from a small cancer into a large, metastatic cancer. Hence, viewing the colon mucosa as much as possible is desirable. The ultimate goal is to use the reconstruction results to mark the area likely unobserved during colonoscopy where polyps may be hidden. Materials and Methods:This paper presents a novel method for reconstructing (i) a 3D virtual colon structure from a sequence of 2D colonoscopic images and (ii) the endoscope camera navigation path. Unlike existing work that focus on reconstruction of accurate colon surface for a computer-aided surgery, this work focuses on estimating the alignment of the colon haustral folds, thickness and protrusion of the folds, and the camera path for computeraided screening colonoscopy to inspect as much as possible of the colon mucosa. Results:On endoscopy video of a synthetic colon model, we achieved at least 73% and 90% accuracy in estimating the directions of camera translation and rotation motions, respectively. The average percentage of the depth error is about 10% (8.5/85.8) of the average depth of all the folds seen in all the image sequences. The average percentage of the circumference error is about 10% (17/178.2) of the average circumference of all the folds seen in all the image sequences. The results are promising and give further insight to address this challenging problem. Conclusions:We present the work on reconstruction of a colon structure and endoscope motion from a colonoscopic image sequence for screening colonoscopy. We overcame several challenges such as the scale difference of consecutive reconstructed models, estimating closed fold contours from tracked fold edges, and registering the models. As future work, we will investigate a multi-frame registration approach to further reduce the depth and circumference estimation errors by registering nearby frames that are more similar in fold shape together.

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Automatic room detection and reconstruction in cluttered indoor environments with complex room layouts

Cited by 160 publications

References 27 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

Reconstruction of a 3D Virtual Colon Structure and Camera Motion for Screening Colonoscopy

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