An efficient and compact 3D local descriptor based on the weighted height image

Abstract 3D local descriptors are the fundamental and essential elements that have been commonly applied in 3D computer vision. This paper proposes a novel and effective 3D local descriptor for describing the 3D local shape. The research focuses on accelerating the descriptor generation by simplifying the Local Reference Frame (LRF) and optimizing the feature space through a Weighted Height Image (WHI). An in-depth theoretical analysis of the LRF is conducted. Then, this study proposes a simplified LRF to reduce the redundant computations of the covariance matrix and share the calculations with the 3D information coding. Besides, the feature space is modeled and analyzed in this paper. Based on the analysis, we propose a weighting function to strengthen the abilities of the feature representation. The experimental results indicate that the proposed WHI descriptor outperforms the state-of-the-art (SOTA) algorithms in terms of accuracy and efficiency. Meanwhile, the compactness of the WHI is about six times more than that of the SOTA algorithms. Moreover, for the application of point cloud registration, the proposed WHI exhibits high effectiveness in terms of both accuracy and real-time capability.

[1]  Jingdao Chen,et al.  Performance evaluation of 3D descriptors for object recognition in construction applications , 2018 .

[2]  Mohammed Bennamoun,et al.  An Accurate and Robust Range Image Registration Algorithm for 3D Object Modeling , 2014, IEEE Transactions on Multimedia.

[3]  Nico Blodow,et al.  Aligning point cloud views using persistent feature histograms , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Federico Tombari,et al.  Unique shape context for 3d data description , 2010, 3DOR '10.

[5]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[6]  Y. Rui,et al.  Learning to Rank Using User Clicks and Visual Features for Image Retrieval , 2015, IEEE Transactions on Cybernetics.

[7]  Takeshi Masuda,et al.  Log-polar height maps for multiple range image registration , 2009, Comput. Vis. Image Underst..

[8]  Mohammed Bennamoun,et al.  A Comprehensive Performance Evaluation of 3D Local Feature Descriptors , 2015, International Journal of Computer Vision.

[9]  Juntong Xi,et al.  Efficient and accurate 3D modeling based on a novel local feature descriptor , 2020, Inf. Sci..

[10]  Jun Yu,et al.  Multi-view ensemble manifold regularization for 3D object recognition , 2015, Inf. Sci..

[11]  Andrew E. Johnson,et al.  Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Luigi di Stefano,et al.  On the repeatability of the local reference frame for partial shape matching , 2011, 2011 International Conference on Computer Vision.

[13]  Federico Tombari,et al.  SHOT: Unique signatures of histograms for surface and texture description , 2014, Comput. Vis. Image Underst..

[14]  R. Bro,et al.  Resolving the sign ambiguity in the singular value decomposition , 2008 .

[15]  Jitendra Malik,et al.  Recognizing Objects in Range Data Using Regional Point Descriptors , 2004, ECCV.

[16]  Zhiguo Cao,et al.  A fast and robust local descriptor for 3D point cloud registration , 2016, Inf. Sci..

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

[18]  Anath Fischer,et al.  3D Point Cloud Registration for Localization Using a Deep Neural Network Auto-Encoder , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Yusheng Xu,et al.  Reconstruction of scaffolds from a photogrammetric point cloud of construction sites using a novel 3D local feature descriptor , 2018 .

[20]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[21]  Anton van den Hengel,et al.  Thrift: Local 3D Structure Recognition , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[22]  Federico Tombari,et al.  Unique Signatures of Histograms for Local Surface Description , 2010, ECCV.

[23]  Jianping Fan,et al.  Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing , 2018, IEEE Transactions on Information Forensics and Security.

[24]  Zhiguo Cao,et al.  TOLDI: An effective and robust approach for 3D local shape description , 2017, Pattern Recognit..

[25]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[26]  Federico Tombari,et al.  Performance Evaluation of 3D Keypoint Detectors , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[27]  Xinyi Le,et al.  A Novel SDASS Descriptor for Fully Encoding the Information of 3D Local Surface , 2017, Inf. Sci..

[28]  Mohammed Bennamoun,et al.  Rotational Projection Statistics for 3D Local Surface Description and Object Recognition , 2013, International Journal of Computer Vision.

[29]  Qingquan Li,et al.  Automated extraction of street-scene objects from mobile lidar point clouds , 2012 .

[30]  Jun Yu,et al.  Click Prediction for Web Image Reranking Using Multimodal Sparse Coding , 2014, IEEE Transactions on Image Processing.

[31]  Yuqing He,et al.  An efficient registration algorithm based on spin image for LiDAR 3D point cloud models , 2015, Neurocomputing.

[32]  Tao Zhang,et al.  BRoPH: An efficient and compact binary descriptor for 3D point clouds , 2018, Pattern Recognit..

[33]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[34]  Nicolas David,et al.  Towards 3D lidar point cloud registration improvement using optimal neighborhood knowledge , 2013 .

[35]  Xiong Fengguang,et al.  A 3D Surface Matching Method Using Keypoint- Based Covariance Matrix Descriptors , 2017, IEEE Access.

[36]  Mark A. Richardson,et al.  Local feature based automatic target recognition for future 3D active homing seeker missiles , 2018 .

[37]  Meng Wang,et al.  Image-Based Three-Dimensional Human Pose Recovery by Multiview Locality-Sensitive Sparse Retrieval , 2015, IEEE Transactions on Industrial Electronics.

[38]  Guanghui Liu,et al.  A 3D Descriptor based on Local Height Image , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[39]  Mohammed Bennamoun,et al.  On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes , 2009, International Journal of Computer Vision.

[40]  Michael G. Strintzis,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Snapshots: A Novel Local Surface , 2022 .

[41]  Yulan Guo,et al.  RoPS: A local feature descriptor for 3D rigid objects based on rotational projection statistics , 2013, 2013 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA).

[42]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[43]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Mohammed Bennamoun,et al.  3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[45]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[46]  Alberto Del Bimbo,et al.  Content-Based Retrieval of 3-D Objects Using Spin Image Signatures , 2007, IEEE Transactions on Multimedia.

[47]  Qingming Huang,et al.  Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[48]  Jun Yu,et al.  Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Zhiguo Cao,et al.  Toward the Repeatability and Robustness of the Local Reference Frame for 3D Shape Matching: An Evaluation , 2018, IEEE Transactions on Image Processing.

[50]  Marc Levoy,et al.  A volumetric method for building complex models from range images , 1996, SIGGRAPH.