Segmenting Point Clouds

point cloudA team of researchers from Oregon State University have developed a promising new approach to segmenting point clouds.

New technologies such as lidar enable the rapid collection of massive datasets to model a 3D scene as a point cloud. However, while hardware technology continues to advance, processing 3D point clouds into informative models remains complex and time consuming.

A common approach to increase processing efficiently is to segment the point cloud into smaller sections. This paper proposes a novel approach for point cloud segmentation using computer vision algorithms to analyze panoramic representations of individual laser scans. These panoramas can be quickly created using an inherent neighborhood structure that is established during the scanning process, which scans at fixed angular increments in a cylindrical or spherical coordinate system.

In the proposed approach, a selected image segmentation algorithm is applied on several input layers exploiting this angular structure including laser intensity, range, normal vectors, and color information. These segments are then mapped back to the 3D point cloud so that modeling can be completed more efficiently. This approach does not depend on pre-defined mathematical models and consequently setting parameters for them.

Unlike common geometrical point cloud segmentation methods, the proposed method employs the colorimetric and intensity data as another source of information. The proposed algorithm is demonstrated on several datasets encompassing variety of scenes and objects. Results show a very high perceptual (visual) level of segmentation and thereby the feasibility of the proposed algorithm. The proposed method is also more efficient compared to Random Sample Consensus (RANSAC), which is a common approach for point cloud segmentation.

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2 Responses to Segmenting Point Clouds

  1. Nurunnabi says:

    It looks an interesting paper.
    Info. for readers who are interested in point cloud processing;
    another paper entitled “Robust segmentation for large volumes of laser scanning three -dimensional point cloud data” has been very recently published online-
    in IEEE Transactions on Geoscience and Remote Sensing.

  2. Nurunnabi says:

    Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data.

    By
    Abdul Nurunnabi ; Department of Spatial Sciences, Curtin University, Perth, W.A., Australia ; David Belton ; Geoff West
    Digital Object Identifier 10.1109/TGRS.2016.2551546
    IEEE Transactions on Geoscience and Remote Sensing (Volume:54 , Issue: 8 ),
    pp. 4790 – 4805.

    Abstract— This paper investigates the problems of outliers and/or noise in surface segmentation, and proposes a statistically robust segmentation algorithm for laser scanning 3D point cloud data. Principal Component Analysis (PCA) based local saliency features e.g. normal and curvature have been used frequently in many ways for point cloud segmentation. However, PCA is sensitive to outliers, saliency features from PCA are non-robust and inaccurate in the presence of outliers; consequently segmentation results can be erroneous and unreliable. As a remedy, robust techniques e.g. RANSAC and/or robust versions of PCA have been proposed. However RANSAC is influenced by the well-known swamping effect and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced Maximum Consistency with Minimum Distance (MCMD) based Robust Diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning datasets show that the RDPCA based method has an intrinsic ability to deal with outlier and/or noise contaminated data. Results for a synthetic dataset show that RDPCA is 105 times faster than RPCA, and gives more accurate and robust results when compared to other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA. Coupled with a segment merging algorithm the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial and aerial laser scanning systems.

    Index Terms— Feature extraction, object modelling, outlier, region growing, robustness, robust normal, segmentation, surface reconstruction.

    For more Info. please visit:
    http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7465797&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7465797

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