{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:50:09Z","timestamp":1761648609132,"version":"3.40.3"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030110147"},{"type":"electronic","value":"9783030110154"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-11015-4_49","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T06:42:47Z","timestamp":1548312167000},"page":"645-661","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["A Deeper Look at 3D Shape Classifiers"],"prefix":"10.1007","author":[{"given":"Jong-Chyi","family":"Su","sequence":"first","affiliation":[]},{"given":"Matheus","family":"Gadelha","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Subhransu","family":"Maji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Aytar, Y., Vondrick, C., Torralba, A.: SoundNet: learning sound representations from unlabeled video. In: Neural Information Processing Systems (NIPS) (2016)","DOI":"10.1109\/CVPR.2016.18"},{"key":"49_CR2","unstructured":"Blender Online Community: Blender - a 3D modelling and rendering package. Blender Foundation, Blender Institute, Amsterdam. http:\/\/www.blender.org"},{"key":"49_CR3","unstructured":"Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Generative and discriminative voxel modeling with convolutional neural networks. arXiv preprint arXiv:1608.04236 (2016)"},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Bucilu\u01ce, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2006)","DOI":"10.1145\/1150402.1150464"},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Gadhela, M., Maji, S., Wang, R.: Unsupervised 3D shape induction from 2D views of multiple objects. In: International Conference on 3D Vision 2018 (2017)","DOI":"10.1109\/3DV.2017.00053"},{"key":"49_CR6","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)"},{"key":"49_CR7","doi-asserted-by":"crossref","unstructured":"Gupta, S., Hoffman, J., Malik, J.: Cross modal distillation for supervision transfer. In: Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.309"},{"key":"49_CR8","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"49_CR9","unstructured":"Hegde, V., Zadeh, R.: FusionNet: 3d object classification using multiple data representations. arXiv preprint arXiv:1607.05695 (2016)"},{"key":"49_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling knowledge in a neural network. In: Neural Information Processing Systems (NIPS) (2014)"},{"key":"49_CR11","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML) (2015)"},{"key":"49_CR12","doi-asserted-by":"crossref","unstructured":"Kalogerakis, E., Averkiou, M., Maji, S., Chaudhuri, S.: 3D shape segmentation with projective convolutional networks (2017)","DOI":"10.1109\/CVPR.2017.702"},{"key":"49_CR13","unstructured":"Kanezaki, A., Matsushita, Y., Nishida, Y.: RotationNet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. arXiv preprint arXiv:1603.06208 (2016)"},{"key":"49_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Klokov, R., Lempitsky, V.: Escape from cells: deep kd-networks for the recognition of 3D point cloud models. In: International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.99"},{"key":"49_CR16","unstructured":"Kurakin, A., Goodfellow, I.J., Bengio, S.: Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016)"},{"key":"49_CR17","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: VoxNet: a 3d convolutional neural network for real-time object recognition. In: IEEE International Conference on Intelligent Robots and Systems (IROS) (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"49_CR18","unstructured":"Meagher, D.J.: Octree encoding: a new technique for the representation, manipulation and display of arbitrary 3-d objects by computer. Electrical and Systems Engineering Department Rensseiaer Polytechnic Institute Image Processing Laboratory (1980)"},{"key":"49_CR19","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"49_CR20","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch (2017)"},{"issue":"6","key":"49_CR21","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1145\/360825.360839","volume":"18","author":"BT Phong","year":"1975","unstructured":"Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18(6), 311\u2013317 (1975)","journal-title":"Commun. ACM"},{"key":"49_CR22","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Neural Information Processing Systems (NIPS) (2017)"},{"key":"49_CR23","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.: Volumetric and multi-view CNNs for object classification on 3d data. In: Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"49_CR24","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.: Volumetric andmulti-view CNNs for object classification on 3d data. In: Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"49_CR25","doi-asserted-by":"crossref","unstructured":"Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: learning deep 3d representations at high resolutions. In: Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.701"},{"issue":"3","key":"49_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"49_CR27","doi-asserted-by":"crossref","unstructured":"Sedaghat, N., Zolfaghari, M., Amiri, E., Brox, T.: Orientation-boosted voxel nets for 3D object recognition. In: British Machine Vision Conference (BMVC) (2017)","DOI":"10.5244\/C.31.97"},{"key":"49_CR28","doi-asserted-by":"crossref","unstructured":"Sfikas, K., Theoharis, T., Pratikakis, I.: Exploiting the panorama representation for convolutional neural network classification and retrieval. In: Eurographics Workshop on 3D Object Retrieval (2017)","DOI":"10.1016\/j.cag.2017.12.001"},{"key":"49_CR29","unstructured":"Shen, Y., Feng, C., Yang, Y., Tian, D.: Neighbors do help: deeply exploiting local structures of point clouds. arXiv preprint arXiv:1712.06760 (2017)"},{"key":"49_CR30","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"49_CR31","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.G.: Multi-view convolutional neural networks for 3D shape recognition. In: International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.114"},{"key":"49_CR32","unstructured":"Su, H., Qi, C., Mo, K., Guibas, L.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Computer Vision and Pattern Recognition (CVPR) (2017)"},{"key":"49_CR33","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199 (2013)"},{"key":"49_CR34","doi-asserted-by":"crossref","unstructured":"Wang, C., Pelillo, M., Siddiqi, K.: Dominant set clustering and pooling for multi-view 3D object recognition. In: British Machine Vision Conference (BMVC) (2017)","DOI":"10.5244\/C.31.64"},{"issue":"4","key":"49_CR35","first-page":"72","volume":"36","author":"PS Wang","year":"2017","unstructured":"Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (SIGGRAPH) 36(4), 72 (2017)","journal-title":"ACM Trans. Graph. (SIGGRAPH)"},{"key":"49_CR36","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. arXiv preprint arXiv:1801.07829 (2018)","DOI":"10.1145\/3326362"},{"key":"49_CR37","unstructured":"Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Computer Vision and Pattern Recognition (CVPR) (2015)"},{"key":"49_CR38","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Neural Information Processing Systems (NIPS) (2017)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-11015-4_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T01:36:28Z","timestamp":1674351388000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-11015-4_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030110147","9783030110154"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-11015-4_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}