Comparison of 3D interest point detectors and descriptors for point cloud fusion

32 citations as recorded by crossref.

1. Evaluating Local Geometric Feature Representations for 3D Rigid Data Matching J. Yang et al. https://doi.org/10.1109/TIP.2019.2959236
2. Toward a Data Fusion Index for the Assessment and Enhancement of 3D Multimodal Reconstruction of Built Cultural Heritage A. Pamart et al. https://doi.org/10.3390/rs15092408
3. Virtual ground truth, and pre-selection of 3D interest points for improved repeatability evaluation of 2D detectors S. Lang et al. https://doi.org/10.1051/matecconf/201927702032
4. Semantic segmentation of 3D LiDAR data using deep learning: a review of projection-based methods A. Jhaldiyal & N. Chaudhary https://doi.org/10.1007/s10489-022-03930-5
5. A Multistation 3D Point Cloud Automated Global Registration and Accurate Positioning Method for Railway Tunnels J. Wang et al. https://doi.org/10.1155/2023/6705090
6. A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching J. Yang et al. https://doi.org/10.1109/TPAMI.2019.2960234
7. Revue des descripteurs tridimensionnels (3D) pour la catégorisation des nuages de points acquis avec un système LiDAR de télémétrie mobile S. Daniel https://doi.org/10.1139/geomatica-2018-0001
8. A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges L. Li et al. https://doi.org/10.1155/2021/9953910
9. Feature-based RGB-D camera pose optimization for real-time 3D reconstruction C. Wang & X. Guo https://doi.org/10.1007/s41095-016-0072-2
10. Alignment of dental depth images from an intraoral scanner M. Ko & S. Park https://doi.org/10.1080/16864360.2018.1486962
11. Performance evaluation of single and cross‐dimensional feature detection and description O. Kechagias‐Stamatis et al. https://doi.org/10.1049/iet-ipr.2019.1523
12. Sensing and Artificial Perception for Robots in Precision Forestry: A Survey J. Ferreira et al. https://doi.org/10.3390/robotics12050139
13. Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm L. Yan et al. https://doi.org/10.3390/s17091979
14. Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure H. Jing et al. https://doi.org/10.1080/00396265.2020.1719753
15. Lokal özellik temelli yöntemler kullanılarak 3B yüz tanıma ve doğruluk analizi M. ATİK & Z. DURAN https://doi.org/10.17341/gazimmfd.715450
16. Automatic co-registration of 3D multi-sensor point clouds R. Persad & C. Armenakis https://doi.org/10.1016/j.isprsjprs.2017.05.014
17. Parallel RANSAC for Point Cloud Registration D. Koguciuk https://doi.org/10.1515/fcds-2017-0010
18. Automatic Registration of Very Low Overlapping Array InSAR Point Clouds in Urban Scenes X. Tong et al. https://doi.org/10.1109/TGRS.2022.3153185
19. 3D model assisted fully automated scanning laser Doppler vibrometer measurements S. Sels et al. https://doi.org/10.1016/j.optlaseng.2016.09.007
20. Globally consistent registration of terrestrial laser scans via graph optimization P. Theiler et al. https://doi.org/10.1016/j.isprsjprs.2015.08.007
21. ActivationNet: Representation Learning to Predict Contact Quality of Interacting 3D Surfaces in Engineering Designs R. Ranade & J. Pathak https://doi.org/10.1115/1.4053811
22. An assessment of 2-D and 3-D interest point detectors in volumetric images C. Nur Öztürk https://doi.org/10.1016/j.eswa.2024.124237
23. Dynamic Workpiece Modeling with Robotic Pick-Place Based on Stereo Vision Scanning Using Fast Point-Feature Histogram Algorithm Q. Do et al. https://doi.org/10.3390/app112311522
24. Lernende Roboter für die spanende Bearbeitung leicht zerspanbarer Materialien C. Gollee et al. https://doi.org/10.1515/auto-2021-0162
25. V2PNet: Voxel-to-Point Feature Propagation and Fusion That Improves Feature Representation for Point Cloud Registration H. Hu et al. https://doi.org/10.1109/JSTARS.2023.3278830
26. Keypoint Detection Based on Curvature Grouping and Adaptive Sampling B. Li et al. https://doi.org/10.1109/ACCESS.2024.3439613
27. A Weakly Supervised Graph Deep Learning Framework for Point Cloud Registration L. Sun et al. https://doi.org/10.1109/TGRS.2022.3145474
28. A generalised framework for saliency-based point feature detection M. Brown et al. https://doi.org/10.1016/j.cviu.2016.09.008
29. FPFH Revisited: Histogram Resolutions, Improved Features, and Novel Representation J. Szalai-Gindl & D. Varga https://doi.org/10.1109/ACCESS.2024.3400591
30. Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art P. Ghamisi et al. https://doi.org/10.1109/MGRS.2018.2890023
31. Automatic registration of unordered point clouds acquired by Kinect sensors using an overlap heuristic T. Weber et al. https://doi.org/10.1016/j.isprsjprs.2014.12.014
32. 3D object recognition method with multiple feature extraction from LiDAR point clouds Y. Tian et al. https://doi.org/10.1007/s11227-019-02830-9