Go to the content. | Move to the navigation | Go to the site search | Go to the menu | Contacts | Accessibility

| Create Account

Agresti, Gianluca (2019) Data Driven Approaches for Depth Data Denoising. [Ph.D. thesis]

Full text disponibile come:

[img]
Preview
PDF Document (Ph.D. thesis) - Accepted Version
Available under License Creative Commons Attribution.

25Mb

Abstract (italian or english)

The scene depth is an important information that can be used to retrieve the scene geometry, a missing element in standard color images. For this reason, the depth information is usually employed in many applications such as 3D reconstruction, autonomous driving and robotics.

The last decade has seen the spread of different commercial devices able to sense the scene depth. Among these, Time-of-Flight (ToF) cameras are becoming popular because they are relatively cheap and they can be miniaturized and implemented on portable devices.
Stereo vision systems are the most widespread 3D sensors and they are simply composed by two standard color cameras. However, they are not free from flaws, in particular they fail when the scene has no texture. Active stereo and structured light systems have been developed to overcome this issue by using external light projectors.

This thesis collects the findings of my Ph.D. research, which are mainly devoted to the denoising of depth data. First, some of the most widespread commercial 3D sensors are introduced with their strengths and limitations. Then, some techniques for the quality enhancement of ToF depth acquisition are presented and compared with other state-of-the-art methods. A first proposed method is based on a hardware modification of the standard ToF projector. A second approach instead uses multi-frequency ToF recordings as input of a deep learning network to improve the depth estimation. A particular focus will be given to how the denoising performance degrades, when the network is trained on synthetic data and tested on real data. Thus, a method to reduce the gap in performance will be proposed.
Since ToF and stereo vision systems have complementary characteristics, the possibility to fuse the information coming from these sensors is analysed and a method based on a locally consistent fusion, guided by a learning based reliability measure for the two sensors, is proposed.
A part of this thesis is dedicated to the description of the data acquisition procedures and the related labeling required to collect the datasets we used for the training and evaluation of the proposed methods.

Abstract (a different language)

La profondità della scena è un importante informazione che può essere usata per recuperare la geometria della scena stessa, un elemento mancante nelle semplici immagini a colori. Per questo motivo, questi dati sono spesso usati in molte applicazioni come ricostruzione 3D, guida autonoma e robotica.

L'ultima decade ha visto il diffondersi di diversi dispositivi capaci di stimare la profondità di una scena. Tra questi, le telecamere Time-of-Flight (ToF) stanno diventando sempre più popolari poiché sono relativamente poco costose e possono essere miniaturizzate e implementate su dispositivi portatili. I sistemi a visione stereoscopica sono i sensori 3D più diffusi e sono composti da due semplici telecamere a colori. Questi sensori non sono però privi di difetti, in particolare non riescono a stimare in maniera corretta la profondità di scene prive di texture. I sistemi stereoscopici attivi e i sistemi a luce strutturata sono stati sviluppati per risolvere questo problema usando un proiettore esterno.

Questa tesi presenta i risultati che ho ottenuto durante il mio Dottorato di Ricerca presso l'Università degli Studi di Padova. Lo scopo principale del mio lavoro è stato quello di presentare metodi per il miglioramento dei dati 3D acquisiti con sensori commerciali. Nella prima parte della tesi i sensori 3D più diffusi verranno presentati introducendo i loro punti di forza e debolezza. In seguito verranno descritti dei metodi per il miglioramento della qualità dei dati di profondità acquisiti con telecamere ToF. Un primo metodo sfrutta una modifica hardware del proiettore ToF. Il secondo utilizza una rete neurale convoluzionale (CNN) che sfrutta dati acquisiti da una telecamera ToF per stimare un'accurata mappa di profondità della scena. Nel mio lavoro è stata data attenzione a come le prestazioni di questo metodo peggiorano quando la CNN è allenata su dati sintetici e testata su dati reali. Di conseguenza, un metodo per ridurre tale perdita di prestazioni verrà presentato. Poiché le mappe di profondità acquisite con sensori ToF e sistemi stereoscopici hanno proprietà complementari, la possibilità di fondere queste due sorgenti di informazioni è stata investigata. In particolare, è stato presentato un metodo di fusione che rinforza la consistenza locale dei dati e che sfrutta una stima dell'accuratezza dei due sensori, calcolata con una CNN, per guidare il processo di fusione. Una parte della tesi è dedita alla descrizione delle procedure di acquisizione dei dati utilizzati per l'allenamento e la valutazione dei metodi presentati.

Statistiche Download
EPrint type:Ph.D. thesis
Tutor:Zanuttigh, Pietro
Supervisor:Schaefer, Henrik
Ph.D. course:Ciclo 32 > Corsi 32 > INGEGNERIA DELL'INFORMAZIONE > SCIENZA E TECNOLOGIA DELL'INFORMAZIONE
Data di deposito della tesi:29 November 2019
Anno di Pubblicazione:02 December 2019
Key Words:Depth data, Time-of-flight, Stereo vision systems, Structured light, denoising, deep learning, data fusion
Settori scientifico-disciplinari MIUR:Area 09 - Ingegneria industriale e dell'informazione > ING-INF/03 Telecomunicazioni
Struttura di riferimento:Dipartimenti > Dipartimento di Ingegneria dell'Informazione
Codice ID:12169
Depositato il:02 Feb 2021 10:32
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

[1] G. Agresti and P. Zanuttigh, “Combination of spatially-modulated tof and structured light for mpi-free depth estimation,” in Proceedings of European Conference on Computer Vision Workshops (ECCVW), 2018. Cerca con Google

[2] G. Agresti and P. Zanuttigh, “Deep learning for multi-path error removal in tof sensors,” in Proceedings of European Conference on Computer Vision Workshops (ECCVW), 2018. Cerca con Google

[3] G. Agresti, H. Schaefer, P. Sartor, and P. Zanuttigh, “Unsupervised domain adaptation for tof data denoising with adversarial learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5584–5593, 2019. Cerca con Google

[4] M. Biasetton, U. Michieli, G. Agresti, and P. Zanuttigh, “Unsupervised domain adaptation for semantic segmentation of urban scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 2019. Cerca con Google

[5] G. Agresti, L. Minto, G. Marin, and P. Zanuttigh, “Deep learning for confidence information in stereo and tof data fusion,” in Proceedings of International Conference on Computer Vision Workshops (ICCVW), Oct 2017. Cerca con Google

[6] G. Agresti, L. Minto, G. Marin, and P. Zanuttigh, “Stereo and tof data fusion by learning from synthetic data,” Information Fusion, vol. 49, pp. 161–173, 2019. Cerca con Google

[7] B. Curless, “Overview of active vision techniques,” in Proceedings of ACM International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH), vol. 99, 2000. Cerca con Google

[8] Stereolabs, “Zed stereo system website.” https://www.stereolabs.com, Accessed July 29th, 2019. Vai! Cerca con Google

[9] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, 2000. Cerca con Google

[10] P. Zanuttigh, G. Marin, C. Dal Mutto, F. Dominio, L. Minto, and G. M. Cortelazzo, Timeof-Flight and Structured Light Depth Cameras. Springer, 2016. Cerca con Google

[11] R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge university press, 2003. Cerca con Google

[12] A. Fusiello, E. Trucco, and A. Verri, “A compact algorithm for rectification of stereo pairs,” Machine Vision and Applications, vol. 12, no. 1, pp. 16–22, 2000. Cerca con Google

[13] H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1582–1599, 2008. Cerca con Google

[14] R. Szeliski, Computer vision: algorithms and applications. Springer Science & Business Media, 2010. Cerca con Google

[15] H. Hirschm¨uller, “Accurate and efficient stereo processing by semi-global matching and mutual information,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 807–814, IEEE, 2005. Cerca con Google

[16] W. Luo, A. G. Schwing, and R. Urtasun, “Efficient deep learning for stereo matching,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016. Cerca con Google

[17] A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry, “End-to-end learning of geometry and context for deep stereo regression,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 66–75, 2017. Cerca con Google

[18] M. Poggi, D. Pallotti, F. Tosi, and S. Mattoccia, “Guided stereo matching,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 979–988, 2019. Cerca con Google

[19] D. Scharstein, H. Hirschm¨uller, Y. Kitajima, G. Krathwohl, N. Neˇsi´c, X.Wang, and P.Westling, “High-resolution stereo datasets with subpixel-accurate ground truth,” in Proceedings of German conference on pattern recognition, pp. 31–42, Springer, 2014. Cerca con Google

[20] Intel, “Intel realsense ds435 active stereo system website.” https://www.intelrealsense.com/depth-camera-d435, Accessed July 31st, 2019. Vai! Cerca con Google

[21] G. C. Birch, A. L. Dagel, B. A. Kast, and C. S. Smith, “3d imaging with structured illumination for advanced security applications,” tech. rep., Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2015. Cerca con Google

[22] J. Geng, “Structured-light 3d surface imaging: a tutorial,” Advances in Optics and Photonics, vol. 3, no. 2, pp. 128–160, 2011. Cerca con Google

[23] J. Salvi, S. Fernandez, T. Pribanic, and X. Llado, “A state of the art in structured light patterns for surface profilometry,” Pattern recognition, vol. 43, no. 8, pp. 2666–2680, 2010. Cerca con Google

[24] F. Remondino and D. Stoppa, TOF range-imaging cameras, vol. 68121. Springer, 2013. Cerca con Google

[25] Microsoft, “Kinect azure.” https://azure.microsoft.com/it-it/services/kinect-dk/, Accessed August 16th, 2019. Vai! Cerca con Google

[26] Sony Corporation, “Sony depthsensing website.” https://www.sony-depthsensing.com, Accessed August 14th, 2019. Vai! Cerca con Google

[27] M. Lindner, I. Schiller, A. Kolb, and R. Koch, “Time-of-flight sensor calibration for accurate range sensing,” Computer Vision and Image Understanding, vol. 114, no. 12, pp. 1318–1328, 2010. Cerca con Google

[28] C. Dal Mutto, P. Zanuttigh, and G. M. Cortelazzo, “Probabilistic tof and stereo data fusion based on mixed pixels measurement models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 11, pp. 2260–2272, 2015. Cerca con Google

[29] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013. Cerca con Google

[30] J. Marco, Q. Hernandez, A. Muñoz, Y. Dong, A. Jarabo, M. H. Kim, X. Tong, and D. Gutierrez, “Deeptof: off-the-shelf real-time correction of multipath interference in time-of-flight imaging,” ACM Transactions on Graphics (TOG), vol. 36, no. 6, p. 219, 2017. Cerca con Google

[31] Q. Guo, I. Frosio, O. Gallo, T. Zickler, and J. Kautz, “Tackling 3d tof artifacts through learning and the flat dataset,” in Proceedings of European Conference on Computer Vision (ECCV), 2018. Cerca con Google

[32] S. Su, F. Heide, G. Wetzstein, and W. Heidrich, “Deep end-to-end time-of-flight imaging,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6383–6392, 2018. Cerca con Google

[33] L. Zhang, B. Curless, and S. M. Seitz, “Spacetime stereo: Shape recovery for dynamic scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. II–367, IEEE, 2003. Cerca con Google

[34] D. Freedman, Y. Smolin, E. Krupka, I. Leichter, and M. Schmidt, “Sra: Fast removal of general multipath for tof sensors,” in Proceedings of European Conference on Computer Vision (ECCV), pp. 234–249, Springer, 2014. Cerca con Google

[35] Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1330–1334, 1998. Cerca con Google

[36] M. Gupta and S. K. Nayar, “Micro phase shifting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 813–820, IEEE, 2012. Cerca con Google

[37] “CloudCompare 3d point cloud and mesh processing software open source project.” https://www.danielgm.net/cc/. Accessed August 24th, 2019. Vai! Cerca con Google

[38] The Blender Foundation, “Blender website.” https://www.blender.org/, Accessed July 29th, 2019. Vai! Cerca con Google

[39] Blender Swap, “Blend swap website.” https://www.lendwsap.com/, Accessed August 22nd, 2019. Vai! Cerca con Google

[40] S. Meister, R. Nair, and D. Kondermann, “Simulation of Time-of-Flight Sensors using Global Illumination,” in Vision, Modeling and Visualization (M. Bronstein, J. Favre, and K. Hormann, eds.), The Eurographics Association, 2013. Cerca con Google

[41] The LuxRender Project, “Luxrender website.” https://luxcorerender.org/, Accessed July 29th, 2019. Vai! Cerca con Google

[42] J. Sell and P. O’Connor, “The xbox one system on a chip and kinect sensor,” IEEE Micro, vol. 34, no. 2, pp. 44–53, 2014. Cerca con Google

[43] T. Edeler, K. Ohliger, S. Hussmann, and A. Mertins, “Time-of-flight depth image denoising using prior noise information,” in Proceedings of IEEE International Conference on Signal Processing, pp. 119–122, IEEE, 2010. Cerca con Google

[44] F. Lenzen, H. Sch¨afer, and C. Garbe, “Denoising time-of-flight data with adaptive total variation,” in Proceedings of the International Symposium on Visual Computing, pp. 337–346, Springer, 2011. Cerca con Google

[45] M. Georgiev, R. Bregovi´c, and A. Gotchev, “Time-of-flight range measurement in lowsensing environment: Noise analysis and complex-domain non-local denoising,” IEEE Transactions on Image Processing, vol. 27, no. 6, pp. 2911–2926, 2018. Cerca con Google

[46] R. Whyte, L. Streeter, M. J. Cree, and A. A. Dorrington, “Review of methods for resolving multi-path interference in time-of-flight range cameras,” in IEEE Sensors, pp. 629–632, IEEE, 2014. Cerca con Google

[47] S. Fuchs, “Multipath interference compensation in time-of-flight camera images,” in Proceedings of International Conference on Pattern Recognition (ICPR), pp. 3583–3586, IEEE, 2010. Cerca con Google

[48] S. Fuchs, M. Suppa, and O. Hellwich, “Compensation for multipath in tof camera measurements supported by photometric calibration and environment integration,” in Proceedings of International Conference on Computer Vision Systems, pp. 31–41, Springer, 2013. Cerca con Google

[49] D. Jiménez, D. Pizarro, M. Mazo, and S. Palazuelos, “Modeling and correction of multipath interference in time of flight cameras,” Image and Vision Computing, vol. 32, no. 1, pp. 1–13, 2014. Cerca con Google

[50] A. Kadambi, R. Whyte, A. Bhandari, L. Streeter, C. Barsi, A. Dorrington, and R. Raskar, “Coded time of flight cameras: sparse deconvolution to address multipath interference and recover time profiles,” ACM Transactions on Graphics (TOG), vol. 32, no. 6, p. 167, 2013. Cerca con Google

[51] R. Whyte, L. Streeter, M. J. Cree, and A. A. Dorrington, “Resolving multiple propagation paths in time of flight range cameras using direct and global separation methods,” Optical Engineering, vol. 54, no. 11, p. 113109, 2015. Cerca con Google

[52] N. Naik, A. Kadambi, C. Rhemann, S. Izadi, R. Raskar, and S. Bing Kang, “A light transport model for mitigating multipath interference in time-of-flight sensors,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 73–81, 2015. Cerca con Google

[53] S. Achar, J. R. Bartels, W. L. Whittaker, K. N. Kutulakos, and S. G. Narasimhan, “Epipolar time-of-flight imaging,” ACM Transactions on Graphics (TOG), vol. 36, no. 4, p. 37, 2017. Cerca con Google

[54] A. Bhandari, A. Kadambi, R. Whyte, C. Barsi, M. Feigin, A. Dorrington, and R. Raskar, “Resolving multipath interference in time-of-flight imaging via modulation frequency diversity and sparse regularization,” Optics letters, vol. 39, no. 6, pp. 1705–1708, 2014. Cerca con Google

[55] K. Son, M.-Y. Liu, and Y. Taguchi, “Learning to remove multipath distortions in time-offlight range images for a robotic arm setup,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 3390–3397, 2016. Cerca con Google

[56] A. A. Dorrington and R. Z. Whyte, “Time of flight camera system which resolves direct and multi-path radiation components,” Jan. 23 2018. US Patent 9,874,638. Cerca con Google

[57] S. K. Nayar, G. Krishnan, M. D. Grossberg, and R. Raskar, “Fast separation of direct and global components of a scene using high frequency illumination,” ACM Transactions on Graphics (TOG), vol. 25, no. 3, pp. 935–944, 2006. Cerca con Google

[58] Y. Xu, L. Ekstrand, J. Dai, and S. Zhang, “Phase error compensation for three-dimensional shape measurement with projector defocusing,” Applied Optics, vol. 50, no. 17, pp. 2572–2581, 2011. Cerca con Google

[59] C. Dal Mutto, P. Zanuttigh, and G. Cortelazzo, “A probabilistic approach to tof and stereo data fusion,” in 3DPVT, (Paris, France), May 2010. Cerca con Google

[60] J. Zhu, L. Wang, J. Gao, and R. Yang, “Spatial-temporal fusion for high accuracy depth maps using dynamic mrfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 899–909, 2010. Cerca con Google

[61] C. D. Mutto, P. Zanuttigh, and G. M. Cortelazzo, “Probabilistic tof and stereo data fusion based on mixed pixels measurement models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 11, pp. 2260–2272, 2015. Cerca con Google

[62] M. O’Toole, F. Heide, L. Xiao, M. B. Hullin, W. Heidrich, and K. N. Kutulakos, “Temporal frequency probing for 5d transient analysis of global light transport,” ACM Transactions on Graphics (TOG), vol. 33, no. 4, p. 87, 2014. Cerca con Google

[63] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016. Cerca con Google

[64] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014. Cerca con Google

[65] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 839–846, IEEE, 1998. Cerca con Google

[66] D. Chan, H. Buisman, C. Theobalt, and S. Thrun, “A noise-aware filter for real-time depth upsampling,” in Proceedings of Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications, 2008. Cerca con Google

[67] G. Marin, P. Zanuttigh, and S. Mattoccia, “Reliable fusion of tof and stereo depth driven by confidence measures,” in Proceedings of European Conference on Computer Vision (ECCV), pp. 386–401, Springer, 2016. Cerca con Google

[68] M. Zhang and B. K. Gunturk, “Multiresolution bilateral filtering for image denoising,” IEEE Transactions on Image Processing, vol. 17, no. 12, pp. 2324–2333, 2008. Cerca con Google

[69] M. Gupta, S. K. Nayar, M. B. Hullin, and J. Martin, “Phasor imaging: A generalization of correlation-based time-of-flight imaging,” ACM Transactions on Graphics (TOG), vol. 34, no. 5, p. 156, 2015. Cerca con Google

[70] H. Zhao, O. Gallo, I. Frosio, and J. Kautz, “Loss functions for image restoration with neural networks,” IEEE Transactions on Computational Imaging, vol. 3, no. 1, pp. 47–57, 2017. Cerca con Google

[71] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 249–256, 2010. Cerca con Google

[72] M. Long, Y. Cao, J.Wang, and M. I. Jordan, “Learning transferable features with deep adaptation networks,” in Proceedings of International Conference on Machine Learning (ICML), pp. 97–105, JMLR. org, 2015. Cerca con Google

[73] M. Long, H. Zhu, J. Wang, and M. I. Jordan, “Deep transfer learning with joint adaptation networks,” in Proceedings of International Conference on Machine Learning (ICML) (D. Precup and Y. W. Teh, eds.), vol. 70 of Proceedings of Machine Learning Research, (International Convention Centre, Sydney, Australia), pp. 2208–2217, PMLR, 06–11 Aug 2017. Cerca con Google

[74] P. Morerio, J. Cavazza, and V. Murino, “Minimal-entropy correlation alignment for unsupervised deep domain adaptation,” arXiv preprint arXiv:1711.10288, 2017. Cerca con Google

[75] B. Sun and K. Saenko, “Deep coral: Correlation alignment for deep domain adaptation,” in Proceedings of European Conference on Computer Vision (ECCV), pp. 443–450, Springer, 2016. Cerca con Google

[76] Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou, “Revisiting batch normalization for practical domain adaptation,” arXiv preprint arXiv:1603.04779, 2016. Cerca con Google

[77] F. M. Cariucci, L. Porzi, B. Caputo, E. Ricci, and S. R. Bul`o, “Autodial: Automatic domain alignment layers,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 5077–5085, IEEE, 2017. Cerca con Google

[78] S. Roy, A. Siarohin, E. Sangineto, S. R. Bulo, N. Sebe, and E. Ricci, “Unsupervised domain adaptation using feature-whitening and consensus loss,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9471–9480, 2019. Cerca con Google

[79] Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in Proceedings of International Conference on Machine Learning (ICML), pp. 1180–1189, 2015. Cerca con Google

[80] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of machine Learning Research, vol. 17, no. 1, pp. 2096–2030, 2016. Cerca con Google

[81] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Proceedings of the Neural Information Processing Systems Conference (NIPS), pp. 2672–2680, 2014. Cerca con Google

[82] K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixellevel domain adaptation with generative adversarial networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 7, 2017. Cerca con Google

[83] A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training.,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, p. 5, 2017. Cerca con Google

[84] S. Sankaranarayanan, Y. Balaji, C. D. Castillo, and R. Chellappa, “Generate to adapt: Aligning domains using generative adversarial networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8503–8512, 2018. Cerca con Google

[85] A. Garcia-Garcia, S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez-Gonzalez, and J. Garcia-Rodriguez, “A survey on deep learning techniques for image and video semantic segmentation,” Applied Soft Computing, vol. 70, pp. 41 – 65, 2018. Cerca con Google

[86] M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes dataset for semantic urban scene understanding,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223, 2016. Cerca con Google

[87] G. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A highdefinition ground truth database,” Pattern Recognition Letters, pp. 88–97, 2009. Cerca con Google

[88] G. Neuhold, T. Ollmann, S. Rota Bulo, and P. Kontschieder, “The Mapillary vistas dataset for semantic understanding of street scenes,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 4990–4999, 2017. Cerca con Google

[89] S. R. Richter, V. Vineet, S. Roth, and V. Koltun, “Playing for data: Ground truth from computer games,” in Proceedings of European Conference on Computer Vision (ECCV) (B. Leibe, J. Matas, N. Sebe, and M. Welling, eds.), vol. 9906, pp. 102–118, Springer International Publishing, 2016. Cerca con Google

[90] G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3234–3243, 2016. Cerca con Google

[91] J. Hoffman, D. Wang, F. Yu, and T. Darrell, “FCNs in the wild: Pixel-level adversarial and constraint-based adaptation,” arXiv preprint arXiv:1612.02649, 2016. Cerca con Google

[92] S. Sankaranarayanan, Y. Balaji, A. Jain, S. Nam Lim, and R. Chellappa, “Learning from synthetic data: Addressing domain shift for semantic segmentation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3752–3761, 2018. Cerca con Google

[93] Y. Zhang, P. David, and B. Gong, “Curriculum domain adaptation for semantic segmentation of urban scenes,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 2020–2030, 2017. Cerca con Google

[94] J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. Efros, and T. Darrell, “Cycada: Cycle-consistent adversarial domain adaptation,” in Proceedings of International Conference on Machine Learning (ICML), 2018. Cerca con Google

[95] Y. C. Chen, Y. Y. Lin, M. H. Yang, and J. B. Huang, “Crdoco: Pixel-level domain transfer with cross-domain consistency,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1791–1800, 2019. Cerca con Google

[96] Y.-H. Tsai, W.-C. Hung, S. Schulter, K. Sohn, M.-H. Yang, and M. Chandraker, “Learning to adapt structured output space for semantic segmentation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7472–7481, 2018. Cerca con Google

[97] Y. Luo, L. Zheng, T. Guan, J. Yu, and Y. Yang, “Taking a closer look at domain shift: Category-level adversaries for semantics consistent domain adaptation,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. Cerca con Google

[98] Y. Chen, W. Li, and L. Van Gool, “Road: Reality oriented adaptation for semantic segmentation of urban scenes,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7892–7901, 2018. Cerca con Google

[99] T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. P´erez, “Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2517–2526, 2019. Cerca con Google

[100] P. Z. Ramirez, A. Tonioni, S. Salti, and L. Di Stefano, “Learning across tasks and domains,” arXiv preprint arXiv:1904.04744, 2019. Cerca con Google

[101] Z. Ren and Y. J. Lee, “Cross-domain self-supervised multi-task feature learning using synthetic imagery,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. Cerca con Google

[102] A. Tonioni, M. Poggi, S. Mattoccia, and L. Di Stefano, “Unsupervised adaptation for deep stereo,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 1605– 1613, 2017. Cerca con Google

[103] A.I. Wiki, “A beginner’s guide to generative adversarial networks (gans).” https://skymind.ai/wiki/generative-adversarial-network-gan, Accessed November 24th, Vai! Cerca con Google

2019. Cerca con Google

[104] X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. P. Smolley, “Least squares generative adversarial networks,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 2813–2821, IEEE, 2017. Cerca con Google

[105] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” arXiv preprint, 2017. Cerca con Google

[106] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., “Tensorflow: a system for large-scale machine learning.,” in Symposium on Operating Systems Design and Implementation (OSDI), vol. 16, pp. 265–283, 2016. Cerca con Google

[107] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,”in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440, 2015. Cerca con Google

[108] F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” in Proceedings of International Conference on Learning Representation (ICLR), 2016. Cerca con Google

[109] H. Zhao, J. Shi, X. Qi, X.Wang, and J. Jia, “Pyramid scene parsing network,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881–2890, 2017. Cerca con Google

[110] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, pp. 834–848, 2018. Cerca con Google

[111] W.-C. Hung, Y.-H. Tsai, Y.-T. Liou34, Y.-Y. Lin, and M.-H. Yang15, “Adversarial learning for semi-supervised semantic segmentation,” in Proceedings of British Machine Vision Conference (BMVC), 2018. Cerca con Google

[112] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Doll´ar, and C. L. Zitnick, Cerca con Google

“Microsoft coco: Common objects in context,” in Proceedings of European Conference Cerca con Google

on Computer Vision (ECCV), pp. 740–755, Springer, 2014. Cerca con Google

[113] D. Scharstein, H. Hirschm¨uller, Y. Kitajima, G. Krathwohl, N. Nešić, X.Wang, and P.Westling, “High-resolution stereo datasets with subpixel-accurate ground truth,” in Proceedings of German Conference on Pattern Recognition, pp. 31–42, Springer, 2014. Cerca con Google

[114] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013. Cerca con Google

[115] B. Tippetts, D. Lee, K. Lillywhite, and J. Archibald, “Review of stereo vision algorithms and their suitability for resource-limited systems,” Journal of Real-Time Image Processing, pp. 1–21, 2013. Cerca con Google

[116] X. Hu and P. Mordohai, “A quantitative evaluation of confidence measures for stereo vision,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2121–2133, 2012. Cerca con Google

[117] M. Poggi, F. Tosi, and S. Mattoccia, “Quantitative evaluation of confidence measures in a machine learning world,” in ICCV, 2017. Cerca con Google

[118] M. Poggi and S. Mattoccia, “Learning from scratch a confidence measure,” in Proceedings of British Machine Vision Conference (BMVC), 2016. Cerca con Google

[119] A. Seki and M. Pollefeys, “Patch based confidence prediction for dense disparity map,” in Proceedings of British Machine Vision Conference (BMVC), 2016. Cerca con Google

[120] M. Poggi and S. Mattoccia, “Learning to predict stereo reliability enforcing local consistency of confidence maps,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. Cerca con Google

[121] M. Hansard, S. Lee, O. Choi, and R. Horaud, Time-of-Flight Cameras: Principles, Methods and Applications. Springer Briefs in Computer Science, Springer, 2013. Cerca con Google

[122] F. Remondino and D. Stoppa, eds., TOF Range-Imaging Cameras. Springer, 2013. Cerca con Google

[123] S. A. Gudmundsson, H. Aanaes, and R. Larsen, “Fusion of stereo vision and time of flight imaging for improved 3d estimation,” Int. J. Intell. Syst. Technol. Appl., vol. 5, pp. 425–433, 2008. Cerca con Google

[124] R. Nair, K. Ruhl, F. Lenzen, S. Meister, H. Sch¨afer, C. Garbe, M. Eisemann, M. Magnor, and D. Kondermann, “A survey on time-of-flight stereo fusion,” in Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications (M. Grzegorzek, C. Theobalt, R. Koch, and A. Kolb, eds.), vol. 8200 of Lecture Notes in Computer Science, pp. 105–127, Springer Berlin Heidelberg, 2013. Cerca con Google

[125] J. Zhu, L. Wang, R. Yang, and J. Davis, “Fusion of time-of-flight depth and stereo for high accuracy depth maps,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. Cerca con Google

[126] J. Zhu, L. Wang, J. Gao, and R. Yang, “Spatial-temporal fusion for high accuracy depth maps using dynamic mrfs,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 899–909, 2010. Cerca con Google

[127] J. Zhu, L. Wang, R. Yang, J. E. Davis, and Z. Pan, “Reliability fusion of time-of-flight depth and stereo geometry for high quality depth maps,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 7, pp. 1400–1414, 2011. Cerca con Google

[128] R. Nair, F. Lenzen, S. Meister, H. Schaefer, C. Garbe, and D. Kondermann, “High accuracy tof and stereo sensor fusion at interactive rates,” in Proceedings of European Conference on Computer Vision Workshops (ECCVW), 2012. Cerca con Google

[129] B. Chen, C. Jung, and Z. Zhang, “Variational fusion of time-of-flight and stereo data using edge selective joint filtering,” in Proceedings of IEEE International Conference on Image Processing (ICIP), 2017. Cerca con Google

[130] B. Chen, C. Jung, and Z. Zhang, “Variational fusion of time-of-flight and stereo data for depth estimation using edge selective joint filtering,” IEEE Transactions on Multimedia, pp. 1–1, 2018. Cerca con Google

[131] G. Evangelidis, M. Hansard, and R. Horaud, “Fusion of Range and Stereo Data for High-Resolution Scene-Modeling,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 11, pp. 2178 – 2192, 2015. Cerca con Google

[132] S. Mattoccia, “A locally global approach to stereo correspondence,” in Proceedings of International Conference on Computer Vision Workshops (ICCVW), pp. 1763–1770, IEEE, 2009. Cerca con Google

[133] C. Dal Mutto, P. Zanuttigh, S. Mattoccia, and G. Cortelazzo, “Locally consistent tof and stereo data fusion,” in Proceedings of European Conference on Computer Vision Workshops (ECCVW), pp. 598–607, Springer, 2012. Cerca con Google

[134] D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. Cerca con Google

[135] H. Hirschmuller, “Stereo processing by semiglobal matching and mutual information,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008. Cerca con Google

[136] K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing humanlevel performance on imagenet classification,” in Proceedings of International Conference on Computer Vision (ICCV), pp. 1026–1034, 2015. Cerca con Google

[137] M. D. Zeiler, “Adadelta: an adaptive learning rate method,” arXiv preprint arXiv:1212.5701, 2012. Cerca con Google

[138] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al., “Tensorflow: Large-scale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016. Cerca con Google

[139] Q. Yang, R. Yang, J. Davis, and D. Nister, “Spatial-depth super resolution for range images", in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8, 2007. Cerca con Google

[140] H. H. Ku et al., “Notes on the use of propagation of error formulas,” Journal of Research of the National Bureau of Standards, vol. 70, no. 4, 1966. Cerca con Google

[141] R. Lange, P. Seitz, A. Biber, and S. C. Lauxtermann, “Demodulation pixels in ccd and cmos technologies for time-of-flight ranging,” in Sensors and camera systems for scientific, industrial, and digital photography applications, vol. 3965, pp. 177–189, International Societyfor Optics and Photonics, 2000. Cerca con Google

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record