3D local descriptors：a review

被引：0
|
作者：
Fang B. [1 ]
Ding J. [1 ]
Ma J. [1 ]
Ming D. [1 ]
机构：
[1] School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2022年 / 50卷 / 11期
关键词：
deep learning-based feature; hand-crafted feature; local; reference frame; three dimension;
D O I：
10.13245/j.hust.221101
中图分类号：
学科分类号：
摘要：
Local descriptors in the field of three dimension (3D) computer vision in the past three decades were summarized．Traditional methods of designing 3D manual local descriptors were reviewed，and deep learning-based methods were introduced．First，for 3D manual local features and learned features，an overview of their classification was provided from the perspectives of local reference frame system and 3D data representation，respectively，and some typical methods were highlighted．Then，common datasets of 3D local descriptors were outlined，and the performance of the existing descriptors on each dataset was statistically presented．Finally，some issues worthy of future research in the field of 3D descriptors were discussed. © 2022 Huazhong University of Science and Technology. All rights reserved.
引用
收藏
页码：1 / 15
页数：14
相关论文
共 89 条
  • [51] LIN S, CHANDRASEKHAR V, LIN W, 3DHoPD： a fast low-dimensional 3D descriptor[J], IEEE Robotics and Automation Letters, 2, 3, pp. 1472-1479, (2017)
  • [52] THOMPSON E M，, BIASOTTI S．, Description and retrieval of geometric patterns on surface meshes using an edge-based LBP approach[J], Pattern Recognition, 82, pp. 1-15, (2018)
  • [53] CHOY C B，, PARK J, KOLTUN V．, Fully convolutional geometric features[C], Proc of the 2019 IEEE/CVF International Conference on Computer Vision, pp. 8957-8965, (2019)
  • [54] BAI X, LUO Z，, ZHOU L, D3Feat：joint learning of dense detection and description of 3D local features [C], Proc of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6358-6366, (2020)
  • [55] QUAN S，, MA J，, MA T, Representing local shape geometry from multi-view silhouette perspective：a distinctive and robust binary 3D feature[J], Signal Processing and Image Communication, 65, pp. 67-80, (2018)
  • [56] GU S，, MA Y，, WANG L, Local structural feature description of point cloud by hierarchical projection [C], Proc of the 2020 International Conference on Image，Video Processing and Artificial Intelligence, pp. 11-13, (2020)
  • [57] HAO L, WANG H．, Geometric feature statistics histogram for both real-valued and binary feature representations of 3D local shape[J], Image and Vision Computing, 117, (2022)
  • [58] WANG J, KANG J．, Registration of 3D point clouds using a local descriptor based on grid point normal [J], Applied Optics, 60, pp. 8818-8828, (2021)
  • [59] RUSU R B，, BLODOW N，, MARTON Z C, Aligning point cloud views using persistent feature histograms[C], Proc of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384-3391, (2008)
  • [60] CIRUJEDA P，, MATEO X, CID Y D, MCOV：a covariance descriptor for fusion of texture and shape features in 3D point clouds[C], Proc of the 2014 2nd International Conference on 3D Vision, pp. 551-558, (2014)
123456789