Abstract
The task of detecting the interest points in 3D meshes has typically been handled by geometric methods. These methods, while designed according to human preference, can be ill-equipped for handling the variety and subjectivity in human responses. Different tasks have different requirements for interest point detection; some tasks may necessitate high precision while other tasks may require high recall. Sometimes points with high curvature may be desirable, while in other cases high curvature may be an indication of noise. Geometric methods lack the required flexibility to adapt to such changes. As a consequence, interest point detection seems to be well suited for machine learning methods that can be trained to match the criteria applied on the annotated training data. In this paper, we formulate interest point detection as a supervised binary classification problem using a random forest as our classifier. We validate the accuracy of our method and compare our results to those of five state of the art methods on a new, standard benchmark.
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References
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Computer Graphics Forum 27(2), 643–652 (2008)
Chen, C., Liaw, A., Breiman, L.: Using random forest to learn imbalanced data. Tech. rep., University of California, Berkeley (2004)
Creusot, C., Pears, N., Austin, J.: A machine-learning approach to keypoint detection and landmarking on 3D meshes. IJCV 102(1-3), 146–179 (2013)
Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found. Trends. Comput. Graph. Vis. 7(2-3), 81–227 (2012)
Donner, R., Birngruber, E., Steiner, H., Bischof, H., Langs, G.: Localization of 3D anatomical structures using random forests and discrete optimization. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds.) MICCAI 2010. LNCS, vol. 6533, pp. 86–95. Springer, Heidelberg (2011)
Dutagaci, H., Cheung, C., Godil, A.: Evaluation of 3D interest point detection techniques via human-generated ground truth. The Visual Computer 28, 901–917 (2012)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)
Frome, A., Huber, D., Kolluri, R., Bülow, T., Malik, J.: Recognizing objects in range data using regional point descriptors. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, part III. LNCS, vol. 3023, pp. 224–237. Springer, Heidelberg (2004)
Gal, R., Cohen-Or, D.: Salient geometric features for partial shape matching and similarity. ACM Transactions on Graphics 25(1), 130–150 (2006)
Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Proceedings of the Third Eurographics Symposium on Geometry Processing (2005)
Godil, A., Wagan, A.I.: Salient local 3D features for 3D shape retrieval. arXiv 1105.2796[cs.CV] (2011)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)
Holzer, S., Shotton, J., Kohli, P.: Learning to efficiently detect repeatable interest points in depth data. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 200–213. Springer, Heidelberg (2012)
Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3D scenes. PAMI 21(5), 433–449 (1999)
Knopp, J., Prasad, M., Willems, G., Timofte, R., Van Gool, L.: Hough transform and 3D SURF for robust three dimensional classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part VI. LNCS, vol. 6316, pp. 589–602. Springer, Heidelberg (2010)
Lee, C.H., Varshney, A., Jacobs, D.: Mesh saliency. ACM Transactions on Graphics 24(3), 659–666 (2005)
Lian, Z., Godil, A., Bustos, B., Daoudi, M., Hermans, J., Kawamura, S., Kurita, Y., Lavoué, G., Van Nguyen, H., Ohbuchi, R., et al.: A comparison of methods for non-rigid 3D shape retrieval. Pattern Recognition 46(1), 449–461 (2013)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Matei, B., Shan, Y., Sawhney, H.S., Tan, Y., Kumar, R., Huber, D., Hebert, M.: Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation. PAMI 28(7), 1111–1126 (2006)
Mian, A.S., Bennamoun, M., Owens, R.A.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. IJCV 89(2-3), 348–361 (2010)
Novatnack, J., Nishino, K.: Scale-dependent 3D geometric features. In: ICCV (2007)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
Rusu, R., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: ICRA, pp. 3212–3217 (2009)
Salti, S., Tombari, F., Di Stefano, L.: A performance evaluation of 3D keypoint detectors. In: 3DIMPVT, pp. 236–243 (2011)
Shilane, P., Funkhouser, T.: Distinctive regions of 3D surfaces. ACM Transactions on Graphics 26(2) (2007)
Sipiran, I., Bustos, B.: Harris 3d: a robust extension of the harris operator for interest point detection on 3D meshes. Visual Computer 27(11), 963–976 (2011)
Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Proceedings of the Symposium on Geometry Processing, pp. 1383–1392 (2009)
Surazhsky, V., Surazhsky, T., Kirsanov, D., Gortler, S.J., Hoppe, H.: Fast exact and approximate geodesics on meshes. ACM Transactions on Graphics 24(3), 553–560 (2005)
Taubin, G.: Estimating the tensor of curvature of a surface from a polyhedral approximation. In: ICCV, pp. 902–907 (1995)
Yu, T.H., Woodford, O.J., Cipolla, R.: A performance evaluation of volumetric 3D interest point detectors. IJCV 102(1-3), 180–197 (2013)
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Teran, L., Mordohai, P. (2014). 3D Interest Point Detection via Discriminative Learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8689. Springer, Cham. https://doi.org/10.1007/978-3-319-10590-1_11
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DOI: https://doi.org/10.1007/978-3-319-10590-1_11
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