Abstract
In this paper, a novel framework to retrieve 3D articulated models from a database based on one or few depth images is presented. Existing state-of-the-arts retrieval approaches usually constrain the view points of query images or assume that the target models are rigid-body. When they are applied to retrieving articulated models, the retrieved results are substantially influenced by the model postures. In our work, we extracts the limbs and torso regions from projections and analyzes the features of local regions. The use of both global and local features can alleviate the disturbance of model postures in model retrieval. Experiments show that the proposed method can efficiently retrieve relevant models within a second, and provides higher retrieval accuracy than those of compared methods for not only rigid body 3D models but also models with articulated limbs.
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References
Lin, J.Y., She, M.F., Tsai, M.H., Lin, I.C., Lau, Y.C., Liu, H.H.: Retrieving 3D objects with articulated limbs by depth image input. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, INSTICC, pp. 101–111. SciTePress (2018)
Canterakis, N.: 3D Zernike moments and Zernike affine invariants for 3D image analysis and recognition. In: 11th Scandinavian Conference on Image Analysis In 11th Sc, pp. 85–93 (1999)
Bai, X., Latecki, L.J.: Path similarity skeleton graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1282–1292 (2008)
ASUS Inc.: Xtion pro (2011). www.asus.com/3D-Sensor/
Microsoft Corp.: Kinect (2011). www.xbox.com/Kinect
Yap, P.T., Paramesran, R., Ong, S.H.: Image analysis by Krawtchouk moments. Trans. Image Process. 12(11), 1367–1377 (2003)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Chen, D.Y., Tian, X.P., Shen, Y.T., Ouhyoung, M.: On visual similarity based 3D model retrieval. Eurographics 22(3), 223–232 (2003)
Daras, P., Axenopoulos, A.: A 3D shape retrieval framework supporting multimodal queries. Int. J. Comput. Vis. 89(2–3), 229–247 (2010)
Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3), 309–314 (2004)
Lin, I.C., Lan, Y.C., Cheng, P.W.: SI-Cut: Structural inconsistency analysis for image foreground extraction. IEEE Trans. Vis. Comput. Graph. 21(7), 860–872 (2015)
Lian, Z., Zhang, J.: SHREC15 non-rigid 3D shape retrieval (2015). www.icst.pku.edu.cn/zlian/shrec15-non-rigid/data.html
Autodesk Inc.: Maya (1998). www.autodesk.com/products/maya/
Shen, W., Wang, Y., Bai, X., Wang, H., Jan Latecki, L.: Shape clustering: common structure discovery. Pattern Recogn. 46(2), 539–550 (2013)
Chen, C.H., Tsai, M.H., Lin, I.C., Lu, P.H.: Skeleton-driven surface deformation through lattices for real-time character animation. Vis. Comput. 29(4), 241–251 (2013)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Confererence on Computer Vision, pp. 945–953 (2015)
Funkhouser, T., et al.: A search engine for 3D models. ACM Trans. Graph. 22(1), 83–105 (2003)
Wu, L.C., Lin, I., Tsai, M.H.: Augmented reality instruction for object assembly based on markerless tracking. In: Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D 2016, pp. 95–102. ACM (2016)
Shen, W., Bai, X., Hu, R., Wang, H., Latecki, L.J.: Skeleton growing and pruning with bending potential ratio. Pattern Recogn. 44(2), 196–209 (2011)
Igarashi, T., Matsuoka, S., Tanaka, H.: Teddy: a sketching interface for 3D freeform design. In: Proceedings of SIGGRAPH, pp. 409–416 (1999)
Wang, Y.S., Lee, T.Y.: Curve-skeleton extraction using iterative least squares optimization. IEEE Trans. Visual Comput. Graphics 14(4), 926–936 (2008)
Mohamed, W., Hamza, A.B.: Reeb graph path dissimilarity for 3D object matching and retrieval. Visual Comput. 28(3), 305–318 (2012)
Kim, V.G., Li, W., Mitra, N.J., Chaudhuri, S., DiVerdi, S., Funkhouser, T.: Learning part-based templates from large collections of 3D shapes. ACM Trans. Graph. 32(4), 70:1–70:12 (2013)
Kim, V.G., Chaudhuri, S., Guibas, L., Funkhouser, T.: Shape2pose: Human-centric shape analysis. ACM Trans. Graph. 33(4), 120:1–120:12 (2014)
Xie, Z., Xiong, Y., Xu, K.: AB3D: action-based 3D descriptor for shape analysis. Vis. Comput. 30(6–8), 591–601 (2014)
Kleiman, Y., van Kaick, O., Sorkine-Hornung, O., Cohen-Or, D.: SHED: shape edit distance for fine-grained shape similarity. ACM Trans. Graph. 34(6), 235:1–235:11 (2015)
López-Sastre, R.J., García-Fuertes, A., Redondo-Cabrera, C., Acevedo-Rodríguez, F.J., Maldonado-Bascón, S.: Evaluating 3D spatial pyramids for classifying 3D shapes. Comput. Graph. 37(5), 473–483 (2013)
Sipiran, I., Bustos, B., Schreck, T.: Data-aware 3D partitioning for generic shape retrieval. Comput. graph. 37(5), 460–472 (2013)
Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vision 40(2), 99–121 (2000)
Acknowledgement
This paper was partially supported by Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., and by the Ministry of Science and Technology, Taiwan, under grant no. 106-2221-E-009 -178 -MY2.
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Lin, JY., She, MF., Tsai, MH., Lin, IC., Lau, YC., Liu, HH. (2019). 3D Articulated Model Retrieval Using Depth Image Input. In: Bechmann, D., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2018. Communications in Computer and Information Science, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-26756-8_2
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