Reference: | [1] A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, pages 1106–1114, 2012. [2] Bay, H., Tuytelaars, T., And Gool, L. J. V. 2006. SURF: Speeded up robust features. In ECCV, 404–417. [3] Canny, J. 1986. A computational approach to edge detection. IEEE TPAMI 8, 6, 679–698. [4] Cao, Y., Wang, H., Wang, C., Li, Z., Zhang, L., and Zhang, L. 2010. Mindfinder: Finding images by sketching. In ACM Multimedia International Conference. [5] Chen, D.-Y., Tian, X.-P., Shen, Y.-T., and Ouhyoung, M. 2003. On visual similarity based 3d model retrieval. Comput. Graph. Forum (Proc. Eurographics) 22, 3, 223–232. [6] Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. Return of the devil in the details: Delving deep into convolutional nets. In Proc. BMVC., 2014. [7] Decarlo, D., Finkelstein, A., Rusinkiewicz, S., and Santella, A. 2003. Suggestive contours for conveying shape. ACM TOG (Proc. SIGGRAPH) 22, 3, 848–855. [8] Dixon, D., Prasad, M., and Hammond, T. 2010. icandraw: Using sketch recognition and corrective feedback to assist a user in drawing human faces. ACM CHI. [9] Eitz, M., Richter, R., Boubekeur, T., Hildebrand, K., and Alexa, M. 2012. Sketch-based shape retrieval. ACM Transactions on Graphics 31, 4, 31:1–31:10. [10] Funkhouser, T., Min, P., Kazhdan, M., Chen, J., Halderman, A., Dobkin, D., and Jacobs, D. 2003. A search engine for 3D models. ACM TOG 22, 1, 83–105. [11] F. Wang, L. Kang, and Y. Li. Sketch-based 3d shape retrieval using convolutional neural networks. In arXiv preprint arXiv:1504.03504, 2015. [12] H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller. Multi-view convolutional neural networks for 3D shape recognition. In ICCV, 2015. [13] M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. CoRR, abs/1311.2901, 2013. [14] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proc. CVPR, 2009. [15] J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. CoRR, abs/1310.1531, 2013. [16] Jun-Yan Zhu, Yong Jae Lee, Alexei A. Efros.2014 AverageExplorer: interactive exploration and alignment of visual data collections. ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH 2014 TOG Volume 33 Issue 4, July 2014 [17] Lee, Y., Zitnick, C., and Cohen, M. 2011. ShadowDraw: real-time user guidance for freehand drawing. ACM TOG (Proc. SIGGRAPH) 30, 4, 27:1–27:10. [18] Li B., Lu Y., Godil A., Schreck T., Aono M., Johan H., Saavedra J. M., Tashiro S.: SHREC’13 track: Large scale sketch-based 3D shape retrieval. In 3DOR (2013), pp. 1–9. [19] L¨O Ffler, J. 2000. Content-based retrieval of 3D models in distributed web databases by visual shape information. In Int’l. Conf. Information Visualization, 82–87. [20] Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. IJCV 60, 2, 91–110. [21] Potcharapol Suteparuk, Emmanuel Tsukerman .Geometric Modeling and Processing Project: Mesh Simplication and Expressive Rendering.2013 [22] Shilane, P., Min, P., Kazhdan, M., and Funkhouser, T. 2004. The Princeton Shape Benchmark. In Proc. Shape Modeling International, 167–178. [23] Siddhartha Chaudhuri , Vladlen Koltun, Data-driven suggestions for creativity support in 3D modeling, ACM SIGGRAPH Asia 2010 papers, December 15-18, 2010, Seoul, South Korea [24] SIVIC, J., AND ZISSERMAN, A. 2003. Video Google: a text retrieval approach to object matching in videos. In ICCV, 1470– 1477. [25] Squire, D., Mueller, W., Mueller, H., and Raki, J. 1999. Content-based query of image databases. In Scand. Conf. on Image Analysis, 143–149.. [26] Sykora ´ , D., Kavan, L., Cˇ Ad´Ik, M., Jamriska ˇ , O., Jacobson, A., Whited, B., Simmons, M., and Sorkinehornung, O. 2014. Ink-and-ray: Bas-relief meshes for adding global illumination effects to hand-drawn characters. ACM Trans. Graphics 33. |