Abstract
Recognition of an object from an image or image sequences is an important task in computer vision. It is an important low-level image processing operation and plays a crucial role in many real-world applications. The challenges involved in object recognition are multi-model, multi-pose, complicated background, and depth variations. Recently developed methods have dealt with these challenges and have reported remarkable results for 3D objects. In this paper, a comprehensive overview of recent advances in 3D object recognition using Convolutional Neural Networks (CNN) has been presented. Along with the latest progress in 3D images, general overview of object recognition of 2D, 2.5D, and 3D images is presented.
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https://www.gartner.com/smarterwithgartner/5-trends-emerge-in-gartner-hype-cycle-for-emerging-technologies-2018/, Released on 16 August, 2018
References
Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for boltzmann machines. Cogn Sci 9(1):147–169
Aggarwal JK, Xia L (2014) Human activity recognition from 3D data: a review. Pattern Recogn Lett 48:70–80
Aldoma A, Marton ZC, Tombari F, Wohlkinger W, Potthast C, Zeisl B, Rusu R, Gedikli S, Vincze M (2012) Tutorial: point cloud library: three-dimensional object recognition and 6 DOF pose estimation. IEEE Robot Autom Mag 19(3):80–91
Andreopoulos A, Tsotsos JK (2013) 50 Years of object recognition: directions forward. Comp Vision Image Underst 117(8):827–891
Ankerst M, Kastenm”uller G, Kriegel HP, Seidl T (1999) 3D shape histograms for similarity search and classification in spatial databases. Proc 6th International Symposium on Spatial Databases 1651:207–226
Arman F, Aggarwal JK (1993) Model-based object recognition in dense-range images—a review. ACM Comput Surv 25(1):5–43
Ayache S, Quénot G (2008) Video corpus annotation using active learning. In: European conference on information retrieval. Springer, pp 187–198
Ba L, Caurana R (2013) Do deep nets really need to be deep?. arXiv:13126184 2014:1–6, arXiv:1312.6184v5
Bai S, Bai X, Zhou Z, Zhang Z, Latecki LJ (2016) GIFT: a real-time and scalable 3D shape search engine. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5023–5032. arXiv:http://arXiv.org/abs/1604.01879
Baltrušaitis T, Robinson P, Morency LP (2016) Openface: an open source facial behavior analysis toolkit. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–10
Bautista CM, Dy CA, Mañalac MI, Orbe RA, Cordel M (2016) Convolutional neural network for vehicle detection in low resolution traffic videos. In: 2016 IEEE region 10 symposium (TENSYMP). IEEE, pp 277–281
Ben-Shabat Y, Lindenbaum M, Fischer A (2017) 3d point cloud classification and segmentation using 3d modified fisher vector representation for convolutional neural networks. arXiv:171108241
Besl PJ, Jain RC (1985) Three-dimensional object recognition. ACM Comput Surv 17(1):75–145
Bespalov D, Regli WC, Shokoufandeh A (2003) Reeb graph based shape retrieval for CAD. In: Volume 1: 23rd computers and information in engineering conference, parts a and b, ASME, vol 2003, pp 229–238
Bian X, Lim SN, Zhou N (2016) Multiscale fully convolutional network with application to industrial inspection. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–8
Bo L, Ren X, Fox D (2013) Unsupervised feature learning for rgb-d based object recognition. In: Experimental robotics. Springer, pp 387–402
Boureau YL, Ponce J, LeCun Y (2010) A theoretical analysis of feature pooling in visual recognition. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 111–118
Brady J, Nandhakumar N, Aggarwal J (1988) Recent progress in the recognition of objects from range data. In: [1988 Proceedings] 9th international conference on pattern recognition, IEEE Comput. Soc. Press, pp 85–92. https://doi.org/10.1109/ICPR.1988.28178. http://ieeexplore.ieee.org/document/28178/
Bronstein AM, Bronstein MM, Ovsjanikov M (2010) 3D features, surface descriptors, and object descriptors. 3D Imaging, Analysis, and Applications, pp 1–27
Bucak SS, Jin R, Jain AK (2014) Multiple kernel learning for visual object recognition: a review. IEEE Trans Pattern Anal Mach Intell 36(7):1354–1369
Byeon YH, Kwak KC (2014) Facial expression recognition using 3D convolutional neural network. Int J Adv Comput Sci Appl 5(12):107–112. https://doi.org/10.14569/IJACSA.2014.051215
Campbell RJ, Flynn PJ (2001) A survey of Free-Form object representation and recognition techniques. Comput Vis Image Underst 81(2):166–210
Chellapilla K, Puri S, Simard P (2006) High performance convolutional neural networks for document processing. In: 10th international workshop on frontiers in handwriting recognition. Suvisoft
Chen L, Wei H, Ferryman J (2013) A survey of human motion analysis using depth imagery. Pattern Recogn Lett 34(15):1995–2006
Choi C, Schwarting W, Delpreto J, Rus D (2018) Learning object grasping for soft robot hands. IEEE Robotics and Automation Letters
Cicirello V, Regli WC (2001) Machining feature-based comparisons of mechanical parts. Proceedings - International Conference on Shape Modeling and Applications, SMI 2001, pp 176–184
Ciresan D, Meier U, Masci J, Schmidhuber J (2011) A committee of neural networks for traffic sign classification. In: The 2011 international joint conference on neural networks. IEEE, vol 1, pp 1918–1921
Cordts M, Omran M, Ramos S, Scharwächter T, Enzweiler M, Benenson R, Franke U, Roth S, Schiele B (2015) The cityscapes dataset. In: CVPR workshop on the future of datasets in vision
Deng J, Ding N, Jia Y, Frome A, Murphy K, Bengio S, Li Y, Neven H, Adam H (2014) Large-scale object classification using label relation graphs. In: European conference on computer vision. Springer, pp 48–64
Fathollahi M, Kasturi R (2016) Autonomous driving challenge: to infer the property of a dynamic object based on its motion pattern. In: Computer vision–ECCV 2016 workshops. Springer, 40–46
Fukushima K (1988) Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural networks 1(2):119–130
Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. Springer, Berlin, pp 267–285. https://doi.org/10.1007/978-3-642-46466-9_18
Furukawa Y, Hernández C et al (2015) Multi-view stereo: a tutorial. Foundations and Trends®;, in Computer Graphics and Vision 9(1-2):1–148
Gao S, Tsang IWH, Chia LT, Zhao P (2010) Local features are not lonely–laplacian sparse coding for image classification. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3555–3561
Glocker B, Izadi S, Shotton J, Criminisi A (2013) Real-time rgb-d camera relocalization. In: 2013 IEEE international symposium on mixed and augmented reality (ISMAR). IEEE, pp 173–179
Gómez M J, García F, Martín D, de la Escalera A, Armingol JM (2015) Intelligent surveillance of indoor environments based on computer vision and 3d point cloud fusion. Expert Syst Appl 42(21):8156– 8171
Gomez-Donoso F, Garcia-Garcia A, Garcia-Rodriguez J, Orts-Escolano S, Cazorla M (2017) Lonchanet: a sliced-based cnn architecture for real-time 3d object recognition. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 412–418
Goodfellow IJ, Warde-Farley D, Mirza M, Courville A, Bengio Y (2013) Maxout networks. arXiv:13024389
Guo Y, Bennamoun M, Sohel F, Lu M, Wan J (2014) 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans Pattern Anal Mach Intell 36(11):2270–2287
Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48. https://doi.org/10.1016/j.neucom.2015.09.116
Gupta S, Girshick R, Arbeláez P, Malik J (2014) Learning rich features from RGB-D images for object detection and segmentation. arXiv:14075736 pp 1–16. arXiv:1407.5736v1
Han F, Reily B, Hoff W, Zhang H (2017) Space-time representation of people based on 3d skeletal data: a review. Comput Vis Image Underst 158:85–105
He K, Zhang X, Ren S, Sun J (2014) Spatial pyramid pooling in deep convolutional networks for visual recognition. In: European conference on computer vision. Springer, pp 346–361
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Hecht-Nielsen R (1989) Theory of the backpropagation neural network. Proceedings Of The International Joint Conference On Neural Networks 1:593–605
Hinton GE, Sejnowski TJ (1986) Learning and releaming in boltzmann machines. Parallel Distrilmted Processing 1
Hinton GE, Salakhutdinov RR (2006a) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507
Hinton GE, Osindero S, Teh YW (2006b) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554
Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154
Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:150203167
Ip CY, Lapadat D, Sieger L, Regli WC (2002) Using shape distributions to compare solid models. Proceedings of the 7th ACM symposium on Solid modeling and applications SMA 02, pp 273–280
Iyer N, Jayanti S, Lou K, Kalyanaraman Y, Ramani K (2004) A multi-scale hierarchical 3D shape reprsentation for similar shape retrieval. Tmce, pp 1–10
Ji S, Yang M, Yu K, Xu W (2013) 3D convacolutional neural networks for human action recognition. IEEE Trans Pattern Anal Mach Intell 35(1):221–31
Johns E, Leutenegger S, Davison AJ (2016) Pairwise decomposition of image sequences for active multi-view recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3813–3822. arXiv:1605.08359
Jourabloo A, Liu X (2016) Large-pose face alignment via cnn-based dense 3d model fitting. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4188–4196
Kanezaki A, Matsushita Y, Nishida Y (2016) Rotationnet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. arXiv:160306208
Kendall A, Cipolla R (2016) Modelling uncertainty in deep learning for camera relocalization. Proceedings of the international conference on robotics and automation (ICRA)
Kendall A, Grimes M, Cipolla R (2015) Posenet: A convolutional network for real-time 6-dof camera relocalization. In: Proceedings of the IEEE international conference on computer vision, pp 2938– 2946
Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480
Kȯrtgen M, Park GJ, Novotni M, Klein R (2003) 3D shape matching with 3D shape contexts. In: Proceedings of The 7th central European seminar on computer graphics 2003, pp 5–17
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst, pp 1097–1105. arXiv:1102.0183
Lai K, Bo L, Ren X, Fox D (2011) A large-scale hierarchical multi-view rgb-d object dataset. In: 2011 IEEE international conference on robotics and automation (ICRA). IEEE, pp 1817–1824
Lazaros N, Sirakoulis GC, Gasteratos A (2008) Review of stereo vision algorithms: from software to hardware. Int J Optoelectron 2(4):435–462
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521 (7553):436–444. arXiv:1312.6184v5
Lee CY, Gallagher PW, Tu Z (2016) Generalizing pooling functions in convolutional neural networks: mixed, gated, and tree. In: Artificial intelligence and statistics, pp 464–472
Li B, Lu Y, Li C, Godil A, Schreck T, Aono M, Burtscher M, Fu H, Furuya T, Johan H et al (2014) Shrec’14 track: extended large scale sketch-based 3d shape retrieval. In: Eurographics workshop on 3d object retrieval, vol 2014
Li B, Zhang T, Xia T (2016) Vehicle detection from 3d lidar using fully convolutional network. arXiv:160807916
Li J, Chen BM, Lee GH (2018) So-net: Self-organizing network for point cloud analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 9397–9406
Li L (2014) Time-of-flight camera–an introduction. Technical white paper (SLOA190B)
Li W, Zhang Z, Liu Z (2010) Action recognition based on a bag of 3d points. In: 2010 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 9–14
Lin M, Chen Q, Yan S (2013) Network in network. arXiv:13124400
Liu S, Giles CL, Ororbia I, Alexander G (2017) Learning a hierarchical latent-variable model of 3d shapes. arXiv:170505994
Liu Z, Zhang C, Tian Y (2016) 3D-based deep convolutional neural network for action recognition with depth sequences. Image Vis Comput 55:93–100
Loncomilla P, Ruiz-del Solar J (2016) Object recognition using local invariant features for robotic applications: a survey. Pattern Recogn 60:499–514
Ma C, Guo Y, Lei Y, An W (2018) Binary volumetric convolutional neural networks for 3-d object recognition. IEEE Trans Instrum Meas (99). Available online
Makantasis K, Karantzalos K, Doulamis A, Loupos K (2015) Deep learning-based man-made object detection from hyperspectral data. In: International symposium on visual computing. Springer, pp 717–727
Mamic G, Bennamoun M (2002) Representation and recognition of 3D free-form objects. Digital Signal Process 12(1):47–76
Mantas J (1986) An overview of character recognition methodologies. Pattern Recogn 19(6):425–430. https://doi.org/10.1016/0031-3203(86)90040-3
Maturana D, Scherer S (2015) Voxnet: a 3d convolutional neural network for real-time object recognition. Iros, pp 922–928
McCormac J, Handa A, Davison A, Leutenegger S (2016) SemanticFusion: dense 3D semantic mapping with convolutional neural networks. arXiv:1609.05130
Meagher D (1982) Geometric modeling using octree encoding. Comput Graphics Image Process 19(2):129–147
Mehta D, Sridhar S, Sotnychenko O, Rhodin H, Shafiei M, Seidel HP, Xu W, Casas D, Theobalt C (2017) Vnect: real-time 3d human pose estimation with a single rgb camera. ACM Trans Graphics (TOG) 36(4):44
Mhaskar H, Liao Q, Poggio T (2016) Learning functions: when is deep better than shallow. arXiv (45):1–12. arXiv:1603.00988
Mian AS, Bennamoun M, Owens R (2004) Automated 3D model-based free-form object recognition. Sens Rev 24(2):206–215
Mian AS, Bennamoun M, Owens RA (2005) Automatic correspondence for 3d modeling: an extensive review. Int J Shape Model 11(02):253–291
Miller A, Jain V, Mundy JL (2011) Real-time rendering and dynamic updating of 3-d volumetric data. In: Proceedings of the fourth workshop on general purpose processing on graphics processing units. ACM, p 8
Muller U, Ben J, Cosatto E, Flepp B, Cun YL (2006) Off-road obstacle avoidance through end-to-end learning. In: Advances in neural information processing systems, pp 739–746
Naguri CR, Bunescu RC (2017) Recognition of dynamic hand gestures from 3d motion data using lstm and cnn architectures. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 1130–1133
Ngiam J, Chen Z, Koh PW, Ng AY (2011) Learning deep energy models. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 1105–1112
Nie D, Cao X, Gao Y, Wang L, Shen D (2016) Estimating ct image from mri data using 3d fully convolutional networks. In: Deep learning and data labeling for medical applications. Springer, pp 170–178
Nie W, Cao Q, Liu A, Su Y (2017) Convolutional deep learning for 3d object retrieval. Multimedia Systems 23(3):325–332
Novotni M, Klein R (2001) A geometric approach to 3d object comparison. In: 2001 International conference on shape modeling and applications, SMI. IEEE, pp 167–175
Ouyang W, Luo P, Zeng X, Qiu S, Tian Y, Li H, Yang S, Wang Z, Xiong Y, Qian C et al (2014) Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection. arXiv:14093505
Papon J, Schoeler M (2015) Semantic pose using deep networks trained on synthetic RGB-D. arXiv:http://arXiv.org/abs/1508.00835
Passalis N, Tefas A (2017) Bag-of-features pooling for deep convolutional neural networks. In: Proceedings of the IEEE international conference on computer vision (to appear)
Poggio T, Mhaskar H, Rosasco L, Miranda B, Liao Q (2016) Why and when can deep – but not shallow – networks avoid the curse of dimensionality: a review. CBMM Memo (58). arXiv:1611.00740
Qi CR, Su H, NieBner M, Dai A, Yan M, Guibas LJ (2016) Volumetric and multi-view CNNs for object classification on 3D data. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 5648–5656. arXiv:1604.03265
Qi CR, Su H, Mo K, Guibas LJ (2017a) Pointnet: deep learning on point sets for 3d classification and segmentation. Proc Computer Vision And Pattern Recognition (CVPR) IEEE 1(2):4
Qi CR, Yi L, Su H, Guibas LJ (2017b) Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in neural information processing systems, pp 5099–5108
Quadros A, Underwood JP, Douillard B (2012) An occlusion-aware feature for range images. In: 2012 IEEE international conference on robotics and automation (ICRA). IEEE, pp 4428–4435
Ranzato M, Poultney C, Chopra S, LeCun Y (2006) Efficient learning of sparse representations with an energy-based model. In: Proceedings of the 19th international conference on neural information processing systems. MIT Press, pp 1137–1144
Rastegari M, Ordonez V, Redmon J, Farhadi A (2016) Xnor-net: imagenet classification using binary convolutional neural networks. In: European conference on computer vision. Springer, pp 525–542
Riegler G, Ulusoys AO, Geiger A (2016) Octnet: learning deep 3d representations at high resolutions. arXiv:161105009
Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: explicit invariance during feature extraction. In: Proceedings of the 28th international conference on international conference on machine learning. Omnipress, pp 833–840
Roth HR, Lu L, Seff A, Cherry KM, Hoffman J, Wang S, Liu J, Turkbey E, Summers RM (2014) A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Medical image computing and computer-assisted intervention – MICCAI 2014: 17th international conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I i:520–527, arXiv:1406.2639
Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. In: Artificial intelligence and statistics, pp 448–455
Salvi J, Matabosch C, Fofi D, Forest J (2007) A review of recent range image registration methods with accuracy evaluation. Image Vis Comput 25 (5):578–596
Scherer D, Müller A, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. Artificial Neural Networks–ICANN 2010:92–101
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117. arXiv:1404.7828
Schȯlkopf B, Burges CJC, Smola AJ (eds) (1999) Advances in kernel methods: support vector learning. MIT Press, Cambridge
Schwarz M, Schulz H, Behnke S (2015) RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features. IEEE international conference on robotics and automation (ICRA’15) (May): 1329–1335
Sedaghat N, Zolfaghari M, Brox T (2016) Orientation-boosted Voxel nets for 3D object recognition. arXiv:160403351 [csCV] pp 1–22
Shahroudy A, Liu J, Ng TT, Wang G (2016) Ntu rgb+ d: a large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1010–1019
Shen Y, Feng C, Yang Y, Tian D (2018) Mining point cloud local structures by kernel correlation and graph pooling. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 4
Shilane P, Min P, Kazhdan M, Funkhouser T (2004) The princeton shape benchmark. In: Shape modeling applications, 2004. Proceedings, IEEE, pp 167–178
Siddiqi K, Zhang J, Macrini D, Shokoufandeh A, Bouix S, Dickinson S (2008) Retrieving articulated 3-d models using medial surfaces. Mach Vis Appl 19 (4):261–275
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. Comput Vis–ECCV 2012: 746–760
Simard PY, Steinkraus D, Platt JC, et al. (2003) Best practices for convolutional neural networks applied to visual document analysis. ICDAR 3:958–962
Simonovsky M, Komodakis N (2017) Dynamic edgeconditioned filters in convolutional neural networks on graphs. In: Proc. CVPR
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556
Singh S (2013) Optical character recognition techniques: a survey. Journal of Emerging Trends in Computing and Information Sciences 4(6):545–550
Socher R, Huval B (2012) Convolutional-recursive deep learning for 3D object classification. Advances in Neural ..., (i): 1–9
Sochor J, Juránek R, Herout A (2017) Traffic surveillance camera calibration by 3d model bounding box alignment for accurate vehicle speed measurement. Comput Vis Image Underst 161:87–98
Soltanpour S, Boufama B, Wu QJ (2017) A survey of local feature methods for 3d face recognition. Pattern Recogn 72:391–406
Song S, Xiao J (2015) Deep sliding shapes for Amodal 3D object detection in RGB-D Images. arXiv preprint 8694:1–8, arXiv:1511.02300
Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M (2015) Striving for simplicity: the all convolutional net. Iclr pp 1–14, arXiv:1412.6806
Su H, Maji S, Kalogerakis E, Learned-miller E (2015) Multi-view convolutional neural networks for 3D shape recognition. Ieee Iccv pp 945–953, arXiv:1505.00880
Su H, Qi CR, Li Y, Guibas LJ (2016) Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. In: Proceedings of the IEEE international conference on computer vision. IEEE, vol 11-18-Dece, pp 2686–2694
Suetens P (2017) Fundamentals of medical imaging. Cambridge University Press, Cambridge
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9
Tangelder JWH, Veltkamp RC (2008) A survey of content based 3D shape retrieval methods. Multimedia Tools and Applications 39(3):441–471
Tombari F, Salti S, Di Stefano L (2013) Performance evaluation of 3D keypoint detectors. Int J Comput Vis 102(1-3):198–220
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 4489–4497
Vidya R, Nasira G, Priyankka RJ (2014) Sparse coding: a deep learning using unlabeled data for high-level representation. In: 2014 World Congress on Computing and communication technologies (WCCCT). IEEE, pp 124–127
Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning. ACM, pp 1096–1103
Wan L, Zeiler M, Zhang S, Cun YL, Fergus R (2013) Regularization of neural networks using dropconnect. In: Proceedings of the 30th international conference on machine learning (ICML-13), pp 1058–1066
Wang PS, Liu Y, Guo YX, Sun CY, Tong X (2017) O-cnn: Octree-based convolutional neural networks for 3d shape analysis. ACM Trans Graph (TOG) 36 (4):72
Wu H, Gu X (2015) Towards dropout training for convolutional neural networks. Neural Netw 71:1–10
Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J (2015) 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1912–1920
Wu Z, Huang Y, Wang L, Wang X, Tan T (2017) A comprehensive study on cross-view gait based human identification with deep cnns. IEEE Trans Pattern Anal Mach Intell 39(2):209–226
Xia L, Chen C, Aggarwal J (2012) View invariant human action recognition using histograms of 3d joints. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW). IEEE, pp 20–27
Xiang Y, Mottaghi R, Savarese S (2014) Beyond pascal: a benchmark for 3d object detection in the wild. In: 2014 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 75–82
Xie Z, Xu K, Shan W, Liu L, Xiong Y, Huang H (2015) Projective feature learning for 3D shapes with multi-view depth images. Comput Graphics Forum 34(7):1–11
Yang J, Yu K, Gong Y, Huang T (2009a) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE conference on vision, computer and pattern recognition, 2009. CVPR 2009. IEEE, pp 794–1801
Yang M, Ji S, Xu W (2009b) Wang j, Detecting human actions in surveillance videos. Proceedings of the TrecVID Video Evaluation Workshop, Lv F
Yuen K, Martin S, Trivedi MM (2016) Looking at faces in a vehicle: a deep cnn based approach and evaluation. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE, pp 649–654
Zeiler MD, Fergus R (2013) Stochastic pooling for regularization of deep convolutional neural networks. arXiv:13013557
Zeiler MD, Krishnan D, Taylor GW, Fergus R (2010) Deconvolutional networks. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 2528–2535. arXiv:1302.1700
Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE Multimedia 19 (2):4–10
Zhao R, Ali H, van der Smagt P (2017) Two-stream rnn/cnn for action recognition in 3d videos. arXiv:170309783
Zhi S, Liu Y, Li X, Guo Y (2017) Toward real-time 3d object recognition: a lightweight volumetric cnn framework using multitask learning. Comput & Graphics
Zhou S, Wu Y, Ni Z, Zhou X, Wen H, Zou Y (2016) Dorefa-net: training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv:160606160
Zhou X, Yu K, Zhang T, Huang TS (2010) Image classification using super-vector coding of local image descriptors. In: European conference on computer vision. Springer, pp 141–154
Zia MZ, Stark M, Schiele B, Schindler K (2013) Detailed 3d representations for object recognition and modeling. IEEE Trans Pattern Anal Mach Intell 35(11):2608–2623
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Singh, R.D., Mittal, A. & Bhatia, R.K. 3D convolutional neural network for object recognition: a review. Multimed Tools Appl 78, 15951–15995 (2019). https://doi.org/10.1007/s11042-018-6912-6
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DOI: https://doi.org/10.1007/s11042-018-6912-6