Adaptive binary simplification method for 3D feature descriptor

doi:10.11772/j.issn.1001-9081.2020091501

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (7): 2062-2069.DOI: 10.11772/j.issn.1001-9081.2020091501

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Adaptive binary simplification method for 3D feature descriptor

LIU Shuangyuan^1,2, ZHENG Wangli³, LIN Yunhan^1,2

1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
2. Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System(Wuhan University of Science and Technology), Wuhan Hubei 430065, China;
3. State Grid Electric Power Research Institute Company Limited, Nanjing Jiangsu 211106, China

Received:2020-09-27 Revised:2020-12-22 Online:2021-07-10 Published:2021-01-20
Supported by:
This work is partially supported by the Hubei Natural Science Foundation Youth Project (2020CFB116), the Scientific Research Program for Young and Middle-Aged Talents of Department of Education of Hubei Province (Q20191108).

面向三维特征描述子的自适应二进制简化方法

刘双元^1,2, 郑王里³, 林云汉^1,2

1. 武汉科技大学计算机科学与技术学院, 武汉 430081;
2. 智能信息处理与实时工业系统湖北省重点实验室(武汉科技大学), 武汉 430081;
3. 国网电力科学研究院有限公司, 南京 211106

通讯作者: 林云汉
作者简介:刘双元(1995-),男,山西忻州人,硕士研究生,主要研究方向:机器人感知、三维视觉;郑王里(1985-),男,福建仙游人,工程师,硕士,主要研究方向:电力通信设备检测;林云汉(1985-),男,福建仙游人,讲师,博士,机器人感知、机器人推理与控制。
基金资助:
湖北省自然科学基金青年项目（2020CFB116）；湖北省教育厅科学研究计划中青年人才项目（Q20191108）。

Abstract

Abstract: In the study of 3-Dimensional (3D) local feature descriptor, it is difficult to strike a balance among accuracy, matching time and memory consumption. To solve this problem, an adaptive binary simplification method for 3D feature descriptor was proposed based on the standard deviation principle in statistical theory. First, different binary feature descriptors were generated by changing the binarization unit length and the number of standard deviations in the simplification model, which were applied into the currently widely used Signature of Histogram of OrienTations (SHOT) descriptor, and the optimal combination of binarization unit length and the number of standard deviations was determined by experiments. Finally, the simplified descriptor under the optimal combination was named Standard Deviation feature descriptor for Signature of Histogram of OrienTations (SD-SHOT). Experimental results show that compared with the SHOT descriptor without simplification, SD-SHOT reduces the key point matching time to 1/15 times and the memory occupancy to 1/32 times of SHOT; compared with the existing mainstream simplification methods such as Binary Feature Descriptor for Signature of Histogram of OrienTations (B-SHOT), SD-SHOT has the optimal comprehensive performance. In addition, the validity of the proposed method is verified in the actual robot sorting scene consisting of five different categories of objects.

Key words: adaptive binary method, 3-Dimensional (3D) feature descriptor, 3-Dimensional (3D) object recognition, point cloud, object sorting

摘要： 在三维（3D）局部特征描述子研究中，准确度、匹配时间以及内存消耗存在此消彼长的问题。针对上述问题，基于统计理论中的标准差原理提出一种面向3D特征描述子的自适应二进制简化方法。首先，通过改变简化模型中二值化单元长度和标准差个数来生成不同的二进制特征描述子；然后，将它们应用到当前被广泛使用的基于签名的方向直方图（SHOT）描述子中，并通过实验确定最优的二值化单元长度和标准差个数的组合；最后，将最优组合下的简化描述子命名为SD-SHOT。实验结果表明，与未进行简化的SHOT描述子相比，SD-SHOT在关键点匹配时间上减少为原来的1/15，内存占有率降低为原来的1/32；与现有主流简化方法如B-SHOT等相比，SD-SHOT的性能达到了综合最优水平。此外，在由五种不同类别的物体构成的实际机器人分拣场景中验证了所提方法的有效性。

关键词: 自适应二进制方法, 三维特征描述子, 三维物体识别, 点云, 物体分拣

CLC Number:

TP391.4

LIU Shuangyuan, ZHENG Wangli, LIN Yunhan. Adaptive binary simplification method for 3D feature descriptor[J]. Journal of Computer Applications, 2021, 41(7): 2062-2069.

刘双元, 郑王里, 林云汉. 面向三维特征描述子的自适应二进制简化方法[J]. 计算机应用, 2021, 41(7): 2062-2069.

References

[1] LIU H,CONG Y,SUN G,et al. Robust 3-D object recognition via view-specific constraint[J]. IEEE Transactions on Systems,Man, and Cybernetics:Systems,2020(Early Access):1-11.
[2] LI D,LIU N,GUO Y,et al. 3D object recognition and pose estimation for random bin-picking using Partition Viewpoint Feature Histograms[J]. Pattern Recognition Letters,2019,128:148-154.
[3] WANG F,LIANG C,RU C,et al. An improved point cloud descriptor for vision based robotic grasping system[J]. Sensors, 2019,19(10):No. 2225.
[4] RUSU R B,BLODOW N,MARTON Z C,et al. Aligning point cloud views using persistent feature histograms[C]//Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE,2008:3384-3391.
[5] RUSU R B, BLODOW N, BEETZ M. Fast Point Feature Histograms (FPFH) for 3D registration[C]//Proceedings of the 2009 IEEE International Conference on Robotics and Automation. Piscataway:IEEE,2009:3212-3217.
[6] TOMBARI F,SALTI S,DI STEFANO L. A combined textureshape descriptor for enhanced 3D feature matching[C]//Proceedings of the 18th IEEE International Conference on Image Processing. Piscataway:IEEE,2011:809-812.
[7] GUO Y,SOHEL F,BENNAMOUN M,et al. TriSI:a distinctive local surface descriptor for 3D modeling and object recognition[C]//Proceedings of the 2013 International Conference on Computer Graphics Theory and Applications/International Conference on Information Visualization Theory and Applications. Setúbal:SciTePress,2013:86-93.
[8] GUO Y,SOHEL F,BENNAMOUN M,et al. Rotational projection statistics for 3D local surface description and object recognition[J]. International Journal of Computer Vision,2013,105(1):63-86.
[9] KECHAGIAS-STAMATIS O,AOUF N. Histogram of distances for local surface description[C]//Proceedings of the 2016 IEEE International Conference on Robotics and Automation. Piscataway:IEEE,2016:2487-2493.
[10] YANG J,CAO Z,ZHANG Q. A fast and robust local descriptor for 3D point cloud registration[J]. Information Sciences,2016, 346/347:163-179.
[11] YANG J,ZHANG Q,CAO Z. Multi-attribute statistics histograms for accurate and robust pairwise registration of range images[J]. Neurocomputing,2017,251:54-67.
[12] YANG J,ZHANG Q,XIAO Y,et al. TOLDI:an effective and robust approach for 3D local shape description[J]. Pattern Recognition,2017,65:175-187.
[13] ZHAO B,LE X,XI J. A novel SDASS descriptor for fully encoding the information of a 3D local surface[J]. Information Sciences,2019,483:363-382.
[14] ZHAO B,XI J. Efficient and accurate 3D modeling based on a novel local feature descriptor[J]. Information Sciences,2020, 512:295-314.
[15] GUO Y,BENNAMOUN M,SOHEL F,et al. A comprehensive performance evaluation of 3D local feature descriptors[J]. International Journal of Computer Vision,2016,116(1):66-89.
[16] 郝雯, 王映辉, 宁小娟, 等. 面向点云的三维物体识别方法综述[J]. 计算机科学,2017,44(9):11-16.(HAO W,WANG Y H, NING X J,et al. Survey of 3D object recognition for point clouds[J]. Computer Science,2017,44(9):11-16.)
[17] PRAKHYA S M,LIU B,LIN W. B-SHOT:a binary feature descriptor for fast and efficient keypoint matching on 3D point clouds[C]//Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway:IEEE, 2015:1929-1934.
[18] PRAKHYA S M,LIU B,LIN W,et al. B-SHOT:a binary 3D feature descriptor for fast keypoint matching on 3D point clouds[J]. Autonomous Robots,2017,41(7):1501-1520.
[19] KECHAGIAS-STAMATIS O,AOUF N,CHERMAK L. B-HoD:a lightweight and fast binary descriptor for 3D object recognition and registration[C]//Proceedings of the IEEE 14th International Conference on Networking, Sensing and Control. Piscataway:IEEE,2017:37-42.
[20] PRAKHYA S M,LIN W,CHANDRASEKHAR V,et al. Low bitrate 3D feature descriptors for depth data from Kinect-style sensors[J]. Signal Processing:Image Communication,2017,51:40-49.
[21] DU L,MIN H,LIN Y. CI-SHOT:a statistical method for binary feature descriptor on 3D point clouds[C]//Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics. Piscataway:IEEE,2018:373-379.
[22] LIN Y,SUN Y,MIN H. A general gray code quantized method of binary feature descriptors for fast and efficient keypoint matching[C]//Proceedings of the 2nd International Conference on Intelligent Autonomous Systems. Piscataway:IEEE,2019:1-7.
[23] LIN Y,MIN H,ZHOU H,et al. A human-robot-environment interactive reasoning mechanism for object sorting robot[J]. IEEE Transactions on Cognitive and Developmental Systems,2018,10(3):611-623.
[24] LIN Y,ZHOU H,CHEN M,et al. Automatic sorting system for industrial robot with 3D visual perception and natural language interaction[J]. Measurement and Control, 2019, 52(1/2):100-115.
[25] TOMBARI F,SALTI S,DI STEFANO L. Performance evaluation of 3D keypoint detectors[J]. International Journal of Computer Vision,2013,102(1/2/3):198-220.

Adaptive binary simplification method for 3D feature descriptor

面向三维特征描述子的自适应二进制简化方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	GAO Gong, YANG Hongyu, LIU Hong. 3D point cloud face recognition based on deep learning [J]. Journal of Computer Applications, 2021, 41(9): 2736-2740.
[2]	LUAN Jianing, ZHANG Wei, SUN Wei, ZHANG Ao, HAN Dong. High-accuracy localization algorithm based on fusion of two-dimensional code vision and laser lidar [J]. Journal of Computer Applications, 2021, 41(5): 1484-1491.
[3]	LI Weiqiang, LEI Hang, ZHANG Jingyu, WANG Xupeng. 3D hand pose estimation based on label distribution learning [J]. Journal of Computer Applications, 2021, 41(2): 550-555.
[4]	ZHU Xincheng, HE Kunjin, NI Na, HAO Bo. Rapid calculation method of orthopedic plate fit based on improved iterative closest point algorithm [J]. Journal of Computer Applications, 2021, 41(10): 3033-3039.
[5]	PEI Yiyao, GUO Huiming, ZHANG Danpu, CHEN Wenbo. Robust 3D object detection method based on localization uncertainty [J]. Journal of Computer Applications, 2021, 41(10): 2979-2984.
[6]	ZHAO Qing, YU Yuanhui. 3D face recognition based on hierarchical feature network [J]. Journal of Computer Applications, 2020, 40(9): 2514-2518.
[7]	SONG Yifan, ZHANG Peng, ZONG Libo, MA Bo, LIU Libo. Improved redundant point filtering-based 3D object detection method [J]. Journal of Computer Applications, 2020, 40(9): 2555-2560.
[8]	PENG Yuhui, ZHENG Weihong, ZHANG Jianfeng. Deep learning-based on-road obstacle detection method [J]. Journal of Computer Applications, 2020, 40(8): 2428-2433.
[9]	ZHONG Wenbin, SUN Si, LI Xurui, LIU Guangshuai. Point cloud compression method combining density threshold and triangle group approximation [J]. Journal of Computer Applications, 2020, 40(7): 2059-2068.
[10]	WANG Benjie, NONG Liping, ZHANG Wenhui, LIN Jiming, WANG Junyi. 3D point cloud classification and segmentation network based on Spider convolution [J]. Journal of Computer Applications, 2020, 40(6): 1607-1612.
[11]	XU Jingzhong, WANG Jiarong. Auto-registration method of ground based building point clouds based on line features and iterative closest point algorithm [J]. Journal of Computer Applications, 2020, 40(6): 1837-1841.
[12]	XIAO Shihua, SANG Nan, WANG Xupeng. 3D point cloud head pose estimation based on deep learning [J]. Journal of Computer Applications, 2020, 40(4): 996-1001.
[13]	ZHAO Hong, LIU Xiangdong, YANG Yongjuan. Indoor robot simultaneous localization and mapping based on RGB-D image [J]. Journal of Computer Applications, 2020, 40(12): 3637-3643.
[14]	XI Zhihong, WANG Hongxu, HAN Shuangquan. Fast mismatch elimination algorithm and map-building based on ORB-SLAM2 system [J]. Journal of Computer Applications, 2020, 40(11): 3289-3294.
[15]	TANG Hui, ZHOU Mingquan, GENG Guohua. Low coverage point cloud registration algorithm based on region segmentation [J]. Journal of Computer Applications, 2019, 39(11): 3355-3360.