Abstract
Edge points on 3D point clouds can clearly convey 3D geometry and surface characteristics, therefore, edge detection is widely used in many vision applications with high industrial and commercial demands. However, the fine-grained edge features are difficult to detect effectively as they are generally densely distributed or exhibit small-scale surface gradients. To address this issue, we present a learning-based edge detection network, named EdgeFormer, which mainly consists of two stages. Based on the observation that spatially neighboring points tend to exhibit high correlation, forming the local underlying surface, we convert the edge detection of the entire point cloud into a point classification based on local patches. Therefore, in the first stage, we construct local patch feature descriptors that describe the local neighborhood around each point. In the second stage, we classify each point by analyzing the local patch feature descriptors generated in the first stage. Due to the conversion of the point cloud into local patches, the proposed method can effectively extract the finer details. The experimental results show that our model demonstrates competitive performance compared to six baselines.
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Data and code availability
Publicly available data are used. The datasets are available at https://deep-geometry.github.io/abc-dataset/ and https://www.shapenet.org/download/parts. The code is available at https://github.com/Xieyifei1229/EdgeFormer.
References
Qi CR, Su H, Mo K et al (2017) Pointnet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Guo MH, Cai JX, Liu ZN et al (2021) Pct: point cloud transformer. Comput Vis Media 7:187–199. https://doi.org/10.1007/s41095-021-0229-5
Tang L, Zhan Y, Chen Z et al (2022) Contrastive boundary learning for point cloud segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 8489–8499
Feng M, Gilani SZ, Wang Y et al (2021) Relation graph network for 3d object detection in point clouds. IEEE Trans Image Process 30:92–107. https://doi.org/10.1109/TIP.2020.3031371
Wang L, Wang F, Yan F et al (2018) Saliency-guide simplification for point-cloud geometry. In: Proceedings of the International Conference on Machine Vision and Applications. Association for Computing Machinery, New York, NY, USA, ICMVA 2018, pp 36–40. https://doi.org/10.1145/3220511.3220523
Zhang L, Sun Q, He Y (2014) Splatting lines: an efficient method for illustrating 3d surfaces and volumes. In: Proceedings of the 18th Meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. Association for Computing Machinery, New York, NY, USA, I3D ’14, pp 135–142. https://doi.org/10.1145/2556700.2556703
Metzer G, Hanocka R, Giryes R et al (2021) Self-sampling for neural point cloud consolidation. ACM Trans Graph. https://doi.org/10.1145/3470645
Pauly M, Keiser R, Gross M (2003) Multi-scale feature extraction on point-sampled surfaces. Comput Graph Forum 22(3):281–289. https://doi.org/10.1111/1467-8659.00675
Xia S, Wang R (2017) A fast edge extraction method for mobile lidar point clouds. IEEE Geosci Remote Sens Lett 14(8):1288–1292. https://doi.org/10.1109/LGRS.2017.2707467
Guo B, Zhang Y, Gao J et al (2022) Sglbp: subgraph-based local binary patterns for feature extraction on point clouds. Comput Graph Forum 41(6):51–66. https://doi.org/10.1111/cgf.14500
Daniels J II, Ochotta T, Ha LK et al (2008) Spline-based feature curves from point-sampled geometry. Vis Comput 24(6):449–462. https://doi.org/10.1007/s00371-008-0223-2
Yu L, Li X, Fu CW et al (2018) Ec-net: an edge-aware point set consolidation network. In: Computer Vision - ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VII. Springer-Verlag, Berlin, Heidelberg, pp 398–414. https://doi.org/10.1007/978-3-030-01234-2_24
Wang X, Xu Y, Xu K et al (2020) Pie-net: parametric inference of point cloud edges. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, NIPS’20
Zhu X, Du D, Chen W et al (2023) Nerve: neural volumetric edges for parametric curve extraction from point cloud. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 13601–13610
Rusu RB, Cousins S (2011) 3D is here: point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation. IEEE, Shanghai, China, pp 1–4. https://doi.org/10.1109/ICRA.2011.5980567
Zhang Y, Geng G, Wei X et al (2016) A statistical approach for extraction of feature lines from point clouds. Comput Graph 56:31–45. https://doi.org/10.1016/j.cag.2016.01.004
Mo K, Zhu S, Chang AX et al (2019) Partnet: a large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Mérigot Q, Ovsjanikov M, Guibas LJ (2011) Voronoi-based curvature and feature estimation from point clouds. IEEE Trans Vis Comput Graph 17(6):743–756. https://doi.org/10.1109/TVCG.2010.261
Demarsin K, Vanderstraeten D, Volodine T et al (2007) Detection of closed sharp edges in point clouds using normal estimation and graph theory. Comput Aided Des 39(4):276–283. https://doi.org/10.1016/j.cad.2006.12.005
Bazazian D, Casas JR, Ruiz-Hidalgo J (2015) Fast and robust edge extraction in unorganized point clouds. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, Adelaide, Australia, pp 1–8. https://doi.org/10.1109/DICTA.2015.7371262
Gumhold S, Wang X, MacLeod R (2001) Feature extraction from point clouds. In: Proceedings of the 10th international meshing roundtable, pp 293–305
Weber C, Hahmann S, Hagen H (2010) Sharp feature detection in point clouds. In: Proceedings of the 2010 Shape Modeling International Conference. IEEE Computer Society, USA, SMI ’10, pp 175–186. https://doi.org/10.1109/SMI.2010.32
Chen H, Huang Y, Xie Q et al (2022) Multiscale feature line extraction from raw point clouds based on local surface variation and anisotropic contraction. IEEE Trans Autom Sci Eng 19(2):1003–1016. https://doi.org/10.1109/TASE.2021.3053006
Daniels JI, Ha LK, Ochotta T et al (2007) Robust smooth feature extraction from point clouds. In: Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007. IEEE Computer Society, USA, SMI ’07, pp 123–136. https://doi.org/10.1109/SMI.2007.32
Ohtake Y, Belyaev A, Seidel HP (2004) Ridge-valley lines on meshes via implicit surface fitting. ACM Trans Graph 23(3):609–612. https://doi.org/10.1145/1015706.1015768
Lin Y, Wang C, Cheng J et al (2015) Line segment extraction for large scale unorganized point clouds. ISPRS J Photogramm Remote Sens 102:172–183. https://doi.org/10.1016/j.isprsjprs.2014.12.027
Stylianou G, Farin G (2004) Crest lines for surface segmentation and flattening. IEEE Trans Vis Comput Graph 10(5):536–544. https://doi.org/10.1109/TVCG.2004.24
Hildebrandt K, Polthier K, Wardetzky M (2005) Smooth feature lines on surface meshes. In: Proceedings of the Third Eurographics Symposium on Geometry Processing. Eurographics Association, Goslar, DEU, SGP ’05, pp 85–90
Gao Q, Yamaguchi Y (2019) Extraction of coherent and smooth feature lines from meshes with fine details. Comput Graph 82:222–231. https://doi.org/10.1016/j.cag.2019.05.020
Wang Q, Sohn H, Cheng JC (2019) Development of high-accuracy edge line estimation algorithms using terrestrial laser scanning. Autom Constr 101:59–71. https://doi.org/10.1016/j.autcon.2019.01.009
Hackel T, Wegner JD, Schindler K (2016) Contour detection in unstructured 3D point clouds. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, NV, USA, pp 1610–1618. https://doi.org/10.1109/CVPR.2016.178
Loizou M, Averkiou M, Kalogerakis E (2020) Learning part boundaries from 3D point clouds. Comput Graph Forum 39(5):183–195. https://doi.org/10.1111/cgf.14078
Matveev A, Rakhimov R, Artemov A et al (2022) Def: deep estimation of sharp geometric features in 3D shapes. ACM Trans Graph. https://doi.org/10.1145/3528223.3530140
Hu Z, Zhen M, Bai X et al (2020) Jsenet: joint semantic segmentation and edge detection network for 3D point clouds. In: Vedaldi A, Bischof H, Brox T et al (eds) Computer vision—ECCV 2020. Springer International Publishing, Cham, pp 222–239
Bazazian D, Parés ME (2021) Edc-net: edge detection capsule network for 3D point clouds. Appl Sci 11(4):1833. https://doi.org/10.3390/app11041833
Himeur CE, Lejemble T, Pellegrini T et al (2021) Pcednet: a lightweight neural network for fast and interactive edge detection in 3D point clouds. ACM Trans Graph. https://doi.org/10.1145/3481804
Bode L, Weinmann M, Klein R (2023) Bounded: neural boundary and edge detection in 3D point clouds via local neighborhood statistics. ISPRS J Photogramm Remote Sens 205:334–351. https://doi.org/10.1016/j.isprsjprs.2023.09.023
Feng YF, Shen LY, Yuan CM et al (2023) Deep shape representation with sharp feature preservation. Comput Aided Des 157:103468. https://doi.org/10.1016/j.cad.2022.103468
Ye Y, Yi R, Gao Z et al (2023) Nef: neural edge fields for 3D parametric curve reconstruction from multi-view images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 8486–8495
Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Guyon I, Luxburg UV, Bengio S et al (eds) Advances in neural information processing systems
Lu D, Xie Q, Gao K et al (2022) 3dctn: 3D convolution-transformer network for point cloud classification. IEEE Trans Intell Transp Syst 23(12):24854–24865. https://doi.org/10.1109/TITS.2022.3198836
Zhao H, Jiang L, Jia J et al (2021) Point transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Montreal, QC, Canada, pp 16239–16248. https://doi.org/10.1109/ICCV48922.2021.01595
Du H, Yan X, Wang J et al (2022) Point cloud upsampling via cascaded refinement network. In: Proceedings of the Asian Conference on Computer Vision (ACCV). Springer, Macao, China, pp 586–601. https://doi.org/10.1007/978-3-031-26319-4_7
Yu X, Rao Y, Wang Z et al (2021) Pointr: diverse point cloud completion with geometry-aware transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Montreal, QC, Canada, pp 12478–12487. https://doi.org/10.1109/ICCV48922.2021.01227
Thürrner G, Wüthrich CA (1998) Computing vertex normals from polygonal facets. J Graph Tools 3(1):43–46. https://doi.org/10.1080/10867651.1998.10487487
Max N (1999) Weights for computing vertex normals from facet normals. J Graph Tools 4(2):1–6. https://doi.org/10.1080/10867651.1999.10487501
Koch S, Matveev A, Jiang Z et al (2019) Abc: a big cad model dataset for geometric deep learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Long Beach, CA, USA, pp 9593–9603. https://doi.org/10.1109/CVPR.2019.00983
Besl PJ, McKay ND (1992) Method for registration of 3-D shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, International Society for Optics and Photonics, pp 586–606. https://doi.org/10.1117/12.57955
Moscoso Thompson E, Arvanitis G, Moustakas K, et al (2019) Shrec’19 track: feature curve extraction on triangle meshes. In: 12th EG Workshop 3D Object Retrieval 2019, Italy, pp 1–8
Johnson A, Hebert M (1998) Surface matching for object recognition in complex three-dimensional scenes. Image Vis Comput 16(9):635–651. https://doi.org/10.1016/S0262-8856(98)00074-2
Frome A, Huber D, Kolluri R et al (2004) Recognizing objects in range data using regional point descriptors. In: Pajdla T, Matas J (eds) Computer Vision—ECCV 2004. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 224–237. https://doi.org/10.1007/978-3-540-24672-5_18
Tombari F, Salti S, Di Stefano L (2010) Unique signatures of histograms for local surface description. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision – ECCV 2010. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 356–369. https://doi.org/10.1007/978-3-642-15558-1_26
Rusu RB, Blodow N, Marton ZC et al (2008) Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 3384–3391. https://doi.org/10.1109/IROS.2008.4650967
Funding
This work was supported by the Shaanxi Science and Technology Association Youth Talent Support Program (Grant number: 20230115) and the National Natural Science Foundation of China (Grant number: 61802311).
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All authors contributed significantly to this work. Yifei Xie experimented with the proposed method. Zhikun Tu conducted comparisons with PBRG, SGLBP, EC-Net, NerVE, and PIE-Net, as well as the ablation studies. Tong Yang processed the experimental results. Yuhe Zhang proposed the main idea. Xinyu Zhou supervised the experiment procedure. All authors read and approved the final manuscript.
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Xie, Y., Tu, Z., Yang, T. et al. EdgeFormer: local patch-based edge detection transformer on point clouds. Pattern Anal Applic 28, 11 (2025). https://doi.org/10.1007/s10044-024-01386-6
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DOI: https://doi.org/10.1007/s10044-024-01386-6