SVLA: A compact supervoxel segmentation method based on local allocation

Abstract With the development of three-dimensional (3D) point cloud acquisition technologies, supervoxels have become increasingly important, as they provide compact and uniform representations. In this study, a novel supervoxel segmentation method, supervoxels based on local allocation (SVLA), is proposed. SVLA is composed of three steps, namely extreme point determination, local allocation (LA), and connectivity insurance. LA defines a novel cost function for preserving instance boundaries and enforces local minimization. To test the performance of SVLA, the non-compactness error (NCE) is newly defined to evaluate the compactness, and three commonly used evaluation metrics are employed. Both indoor and outdoor datasets are utilized to perform the experiments. Based on the visual and quantitative analysis of the segmentation results, SVLA demonstrates fulfillment of boundary adherence, compact constraints, and low computational complexity. Compared to state-of-the-art algorithms, SVLA yields superior results, especially with regard to indoor point clouds.

[1]  Gholamreza Akbarizadeh,et al.  A New Statistical-Based Kurtosis Wavelet Energy Feature for Texture Recognition of SAR Images , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Mohammed Bennamoun,et al.  NormalNet: A voxel-based CNN for 3D object classification and retrieval , 2019, Neurocomputing.

[3]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Abdul Nurunnabi,et al.  Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Gholamreza Akbarizadeh,et al.  Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier , 2018, Journal of the Indian Society of Remote Sensing.

[6]  Yicong Zhou,et al.  Differential Evolutionary Superpixel Segmentation , 2018, IEEE Transactions on Image Processing.

[7]  Rainer Stiefelhagen,et al.  Measuring and evaluating the compactness of superpixels , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[8]  Luc Van Gool,et al.  SEEDS: Superpixels Extracted Via Energy-Driven Sampling , 2012, International Journal of Computer Vision.

[9]  J. Demantké,et al.  DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS , 2012 .

[10]  Michela Bertolotto,et al.  Octree-based region growing for point cloud segmentation , 2015 .

[11]  Shuowen Hu,et al.  Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications , 2016 .

[12]  Gholamreza Akbarizadeh,et al.  Integration of Spectral Histogram and Level Set for Coastline Detection in SAR Images , 2019, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Lin Wang,et al.  Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds , 2012, Pattern Recognit..

[14]  Marc Pollefeys,et al.  Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark , 2017, ArXiv.

[15]  Florentin Wörgötter,et al.  Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Manuel Menezes de Oliveira Neto,et al.  Real-time detection of planar regions in unorganized point clouds , 2015, Pattern Recognit..

[17]  Xuelong Li,et al.  Lazy Random Walks for Superpixel Segmentation , 2014, IEEE Transactions on Image Processing.

[18]  Ahmad Kamal Aijazi,et al.  Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation , 2013, Remote. Sens..

[19]  Gholamreza Akbarizadeh,et al.  PolSAR image segmentation based on feature extraction and data compression using Weighted Neighborhood Filter Bank and Hidden Markov random field-expectation maximization , 2020 .

[20]  Ling Shao,et al.  Real-Time Superpixel Segmentation by DBSCAN Clustering Algorithm , 2016, IEEE Transactions on Image Processing.

[21]  Jitendra Malik,et al.  Normalized Cuts and Image Segmentation , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Honggu Lee,et al.  Boundary-enhanced supervoxel segmentation for sparse outdoor LiDAR data , 2014 .

[23]  Silvio Savarese,et al.  3D Semantic Parsing of Large-Scale Indoor Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Bruno Vallet,et al.  TerraMobilita/iQmulus urban point cloud analysis benchmark , 2015, Comput. Graph..

[25]  Gholamreza Akbarizadeh,et al.  Change detection in SAR images using deep belief network: a new training approach based on morphological images , 2019, IET Image Process..

[26]  Nico Blodow,et al.  Fast Point Feature Histograms (FPFH) for 3D registration , 2009, 2009 IEEE International Conference on Robotics and Automation.

[27]  Gholamreza Akbarizadeh,et al.  A Two-Phase Algorithm Based on Kurtosis Curvelet Energy and Unsupervised Spectral Regression for Segmentation of SAR Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Gholamreza Akbarizadeh,et al.  Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images , 2015, IET Comput. Vis..

[29]  Yusheng Xu,et al.  Voxel-based segmentation of 3D point clouds from construction sites using a probabilistic connectivity model , 2018, Pattern Recognit. Lett..

[30]  Yuan Li,et al.  Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[31]  Huan Ni,et al.  Agglomerative oversegmentation using dual similarity and entropy rate , 2019, Pattern Recognit..

[32]  Johannes Engels,et al.  Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.