Search Results - "Tosi, Fabio"

Real-Time Single Image Depth Perception in the Wild with Handheld Devices by Aleotti, Filippo, Zaccaroni, Giulio, Bartolomei, Luca, Poggi, Matteo, Tosi, Fabio, Mattoccia, Stefano

“…Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from…”
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On the Confidence of Stereo Matching in a Deep-Learning Era: A Quantitative Evaluation by Poggi, Matteo, Kim, Seungryong, Tosi, Fabio, Kim, Sunok, Aleotti, Filippo, Min, Dongbo, Sohn, Kwanghoon, Mattoccia, Stefano

“…Stereo matching is one of the most popular techniques to estimate dense depth maps by finding the disparity between matching pixels on two, synchronized and…”
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Quantitative Evaluation of Confidence Measures in a Machine Learning World by Poggi, Matteo, Tosi, Fabio, Mattoccia, Stefano

“…Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to…”
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Optical Tracking Velocimetry (OTV): Leveraging Optical Flow and Trajectory-Based Filtering for Surface Streamflow Observations by Tauro, Flavia, Tosi, Fabio, Mattoccia, Stefano, Toth, Elena, Piscopia, Rodolfo, Grimaldi, Salvatore

“…Nonintrusive image-based methods have the potential to advance hydrological streamflow observations by providing spatially distributed data at high temporal…”
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Enabling Image-Based Streamflow Monitoring at the Edge by Tosi, Fabio, Rocca, Matteo, Aleotti, Filippo, Poggi, Matteo, Mattoccia, Stefano, Tauro, Flavia, Toth, Elena, Grimaldi, Salvatore

“…Monitoring streamflow velocity is of paramount importance for water resources management and in engineering practice. To this aim, image-based approaches have…”
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On the Deployment of Out-of-the-Box Embedded Devices for Self-Powered River Surface Flow Velocity Monitoring at the Edge by Livoroi, Arsal-Hanif, Conti, Andrea, Foianesi, Luca, Tosi, Fabio, Aleotti, Filippo, Poggi, Matteo, Tauro, Flavia, Toth, Elena, Grimaldi, Salvatore, Mattoccia, Stefano

“…As reported in the recent image velocimetry literature, tracking the motion of sparse feature points floating on the river surface as done by the Optical…”
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On the Synergies Between Machine Learning and Binocular Stereo for Depth Estimation From Images: A Survey by Poggi, Matteo, Tosi, Fabio, Batsos, Konstantinos, Mordohai, Philippos, Mattoccia, Stefano

“…Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm…”
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On the Uncertainty of Self-Supervised Monocular Depth Estimation by Poggi, Matteo, Aleotti, Filippo, Tosi, Fabio, Mattoccia, Stefano

“…Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. Despite the astonishing…”
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Learning Monocular Depth Estimation Infusing Traditional Stereo Knowledge by Tosi, Fabio, Aleotti, Filippo, Poggi, Matteo, Mattoccia, Stefano

“…Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be…”
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Towards Real-Time Unsupervised Monocular Depth Estimation on CPU by Poggi, Matteo, Aleotti, Filippo, Tosi, Fabio, Mattoccia, Stefano

“…Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality…”
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SMD-Nets: Stereo Mixture Density Networks by Tosi, Fabio, Liao, Yiyi, Schmitt, Carolin, Geiger, Andreas

“…Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs…”
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Guided Stereo Matching by Poggi, Matteo, Pallotti, Davide, Tosi, Fabio, Mattoccia, Stefano

“…Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end…”
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Real-Time Self-Adaptive Deep Stereo by Tonioni, Alessio, Tosi, Fabio, Poggi, Matteo, Mattoccia, Stefano, Stefano, Luigi Di

“…Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models,…”
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Learning a confidence measure in the disparity domain from O(1) features by Poggi, Matteo, Tosi, Fabio, Mattoccia, Stefano

“…Depth sensing is of paramount importance for countless applications and stereo represents a popular, effective and cheap solution for this purpose. As…”
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Real-Time Self-Supervised Monocular Depth Estimation Without GPU by Poggi, Matteo, Tosi, Fabio, Aleotti, Filippo, Mattoccia, Stefano

“…Single-image depth estimation represents a longstanding challenge in computer vision and although it is an ill-posed problem, deep learning enabled astonishing…”
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Neural Disparity Refinement by Tosi, Fabio, Aleotti, Filippo, Ramirez, Pierluigi Zama, Poggi, Matteo, Salti, Samuele, Mattoccia, Stefano, Stefano, Luigi Di

“…We propose a framework that combines traditional, hand-crafted algorithms and recent advances in deep learning to obtain high-quality, high-resolution…”
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Confidence Estimation for ToF and Stereo Sensors and Its Application to Depth Data Fusion by Poggi, Matteo, Agresti, Gianluca, Tosi, Fabio, Zanuttigh, Pietro, Mattoccia, Stefano

“…Time-of-Flight (ToF) sensors and stereo vision systems are two widely used technologies for depth estimation. Due to their rather complementary strengths and…”
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Good Cues to Learn From Scratch a Confidence Measure for Passive Depth Sensors by Poggi, Matteo, Tosi, Fabio, Mattoccia, Stefano

“…As reported in the stereo literature, confidence estimation represents a powerful cue to detect outliers as well as to improve depth accuracy. Purposely, we…”
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Distilled Semantics for Comprehensive Scene Understanding from Videos by Tosi, Fabio, Aleotti, Filippo, Ramirez, Pierluigi Zama, Poggi, Matteo, Salti, Samuele, Di Stefano, Luigi, Mattoccia, Stefano

“…Whole understanding of the surroundings is paramount to autonomous systems. Recent works have shown that deep neural networks can learn geometry (depth) and…”
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Depth super-resolution from explicit and implicit high-frequency features by Qiao, Xin, Ge, Chenyang, Zhang, Youmin, Zhou, Yanhui, Tosi, Fabio, Poggi, Matteo, Mattoccia, Stefano

“…Guided depth super-resolution aims at using a low-resolution depth map and an associated high-resolution RGB image to recover a high-resolution depth map…”
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