Pubblications - Journals

• M. Poggi, F. Tosi, S. Mattoccia, "Learning a confidence measure in the disparity domain from O(1) features", Computer Vision and Image Understanding (CVIU) (PDF)
• A.Tonioni, M. Poggi, S. Mattoccia, L. Di Stefano, "Unsupervised Domain Adaptation for Depth Prediction from Images", IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI) (PDF)
• M. Poggi, G. Agresti, F. Tosi, P. Zanuttigh, S. Mattoccia, "Confidence Estimation for ToF and Stereo Sensorsand its Application to Depth Data Fusion", IEEE Sensors (PDF)

Pubblications - Journals

• M. Poggi, F. Aleotti, F. Tosi and S. Mattoccia, "On the uncertainty of self-supervised monocular depth estimation", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), June 16-18, 2020, Seattle, Washington, US. (PDF) [CODE]
• F. Tosi, F. Aleotti, P. Zama Ramirez, M. Poggi, S. Salti, L. Di Stefano and S. Mattoccia, "Distilled Semantics for Comprehensive Scene Understanding from Videos", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), June 16-18, 2020, Seattle, Washington, US. (PDF) [CODE]
• P. L. Dovesi, M. Poggi, L. Andraghetti, M. Martì, H. Kjellstrom, A. Pieropan, S. Mattoccia, "Real-Time Semantic Stereo Matching", accepted at IEEE/RAS International Conference on Robotics and Automation (ICRA 2020), May 31-June 4, 2020, Paris, France. (PDF)
• F. Aleotti, M. Poggi, F. Tosi, S. Mattoccia, "Learning end-to-end scene flow by distilling single tasks knowledge", accepted at the 34th AAAI Conference on Artificial Intelligence, February 7-12, 2020, New York, US. (PDF)
• L. Andraghetti, P. Myriokefalitakis, P. L. Dovesi, B. Luque, M. Poggi, A. Pieropan, S. Mattoccia, "Enhancing self-supervised monocular depth estimation with traditional visual odometry", accepted at 7th International Conference on 3D Vision (3DV 2019), September 16-19, 2019, Quebec City, Canada (PDF)
• M. Poggi, D. Pallotti, F. Tosi, and S. Mattoccia, "Guided Stereo Matching", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), June 16-21, 2019, Long Beach, California, US. (PDF) [CODE]
• F. Tosi, F. Aleotti, M. Poggi and S. Mattoccia, "Learning monocular depth estimation infusing traditional stereo knowledge", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), June 16-21, 2019, Long Beach, California, US. (PDF) [CODE]
• A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia and L. Di Stefano, "Real-time self-adaptive deep stereo", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019), June 16-21, 2019, Long Beach, California, US, ORAL. (PDF) [CODE]
• F. Tosi, M. Poggi, S. Mattoccia, "Leveraging confident points for accurate depth refinement on embedded systems", accepted at The IEEE Embedded Vision Workshop (EVW 2019), June 16, 2019, Long Beach, California, US. (PDF)
• V. Peluso, A. Cipolletta, A. Calimera, M. Poggi, F. Tosi and S. Mattoccia, "Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms", accepted at Design, Automation and Testing in Europe (DATE 2019), March 29-29, 2019, Florence, Italy. (PDF)
• P. Zama Ramirez, M. Poggi, F. Tosi, S. Mattoccia, L. Di Stefano, "Geometry meets semantic for semi-supervised monocular depth estimation", accepted at 14th Asian Conference on Computer Vision (ACCV 2018), December 2-6, 2018, Perth, Australia (PDF) [CODE]
• M. Poggi, F. Tosi, S. Mattoccia, "Learning monocular depth estimation with unsupervised trinocular assumptions", accepted at 6th International Conference on 3D Vision (3DV 2018), September 5-8, 2018, Verona, Italy (PDF) [CODE]
• F.Tosi, M. Poggi, A. Benincasa, S. Mattoccia, "Beyond local reasoning for stereo confidence estimation with deep learning", accepted at the 15th European Conference on Computer Vision (ECCV 2018), September 8-14, 2018, Munich, Germany. (PDF) [CODE]
• M. Poggi, F. Aleotti, F. Tosi, S. Mattoccia, "Towards real-time unsupervised monocular depth estimation on CPU", accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018), October 1-5, 2018, Madrid, Spain. (PDF) [CODE]
• F. Aleotti, F. Tosi, M. Poggi, S. Mattoccia, "Generative Adversarial Networks for unsupervised monocular depth prediction", accepted at 3D Reconstruction in the Wild 2018 (3DRW2018), in conjunction with (ECCV 2018), Munich, Germany, September 14, 2018 (PDF)
• M. Poggi, F. Tosi, S. Mattoccia, "Quantitative evaluation of confidence measures in a machine learning world", accepted at The IEEE International Conference on Computer Vision (ICCV 2017), October 22-29, 2017, Venezia, Italy (PDF) [CODE]
• A.Tonioni, M. Poggi, S. Mattoccia, L. Di Stefano, "Unsupervised Adaptation for Deep Stereo", accepted at The IEEE International Conference on Computer Vision (ICCV 2017), October 22-29, 2017, Venezia, Italy (PDF) [CODE]
• F. Tosi, M. Poggi, A.Tonioni, L. Di Stefano, S. Mattoccia, "Learning confidence measures in the wild", accepted at The 28th British Machine Vision Conference (BMVC 2017), September 5-7, 2017, London, UK (PDF)
• M. Poggi, F. Tosi, S. Mattoccia, "Efficient confidence measures for embedded stereo", accepted at The 19th International Conference on Image Analysis and Processing (ICIAP 2017), September 11-15, 2017, Catania, Italy (PDF)
• M. Poggi, F. Tosi, S. Mattoccia, "Even More Confident predictions with deep machine-learning", accepted at The IEEE Embedded Vision Workshop (EVW 2017), July 21, 2017, Honolulu, Hawaii, US (PDF)
• M. Poggi, S. Mattoccia, "Learning to predict stereo reliability enforcing local consistency of confidence maps", accepted at The IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), July 21-26, 2017, Honolulu, Hawaii, US (PDF) [CODE]
• M. Poggi, S. Mattoccia, "Evaluation of variants of the SGM algorithm aimed at implementation on embedded or reconfigurable devices", accepted at 6th International Conference on 3D Imaging (IC3D2016), December 13-14, 2016, Liège, Belgium (PDF)
• M. Boschini, M. Poggi, S. Mattoccia, "Improving the reliability of 3D people tracking system leveraging on deep-learning", accepted at 6th International Conference on 3D Imaging (IC3D2016), December 13-14, 2016, Liège, Belgium (PDF)
• M. Poggi, S. Mattoccia, "Deep Stereo Fusion: combining multiple disparity hypotheses with deep-learning", accepted at 4th International Conference on 3D Vision (3DV 2016), October 25-28, 2016, Stanford, California, USA (PDF) [CODE]
• M. Poggi, S. Mattoccia, "Learning a general-purpose confidence measure based on O(1) features and a smarter aggregation strategy for semi global matching", accepted at 4th International Conference on 3D Vision (3DV 2016), October 25-28, 2016, Stanford, California, USA, ORAL (PDF) [CODE]
• M. Poggi, S. Mattoccia, "Learning from scratch a confidence measure", accepted at 27th British Machine Vision Conference (BMVC 2016), September 19-22, 2016, York, UK (PDF) [CODE]
• M. Poggi, S. Mattoccia, "A wearable mobility aid for the visually Impaired based on embedded 3D vision and deep learning", First IEEE Workshop on ICT Solutions for eHealth (IEEE ICTS4eHealth 2016) in conjunction with the Twenty-First IEEE Symposium on Computers and Communications, June 27-30, 2016, Messina, Italy (PDF)
• M. Poggi, L. Nanni, S. Mattoccia, "Crosswalk recognition through pointcloud processing and deep-learning suited to a wearable mobility aid for the visually impaired", Image-based Smart City Application (ISCA2015), ICIAP Workshops 2015 : 282-289 (PDF)
• S. Mattoccia, M. Poggi, "A passive RGBD sensor for accurate and real-time depth sensing self-contained into an FPGA", 9th ACM/SIGBED International Conference on Distributed Smart Cameras (ICDSC 2015) (PDF)