Stereo and ToF data fusion by learning from synthetic data
| Autor: | Giulio Marin, Ludovico Minto, Pietro Zanuttigh, Gianluca Agresti |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
02 engineering and technology Convolutional neural network Synthetic data Deep Learning 0202 electrical engineering electronic engineering information engineering Sensor Fusion Stereo Time-of-Flight Deep Learning Time-of-Flight Sensor Fusion business.industry Deep learning 020206 networking & telecommunications Pattern recognition Stereo Sensor fusion Stereopsis Hardware and Architecture Confidence measures Signal Processing Local consistency 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Software Information Systems |
| Zdroj: | Information Fusion. 49:161-173 |
| ISSN: | 1566-2535 |
| DOI: | 10.1016/j.inffus.2018.11.006 |
| Popis: | Time-of-Flight (ToF) sensors and stereo vision systems are both capable of acquiring depth information but they have complementary characteristics and issues. A more accurate representation of the scene geometry can be obtained by fusing the two depth sources. In this paper we present a novel framework for data fusion where the contribution of the two depth sources is controlled by confidence measures that are jointly estimated using a Convolutional Neural Network. The two depth sources are fused enforcing the local consistency of depth data, taking into account the estimated confidence information. The deep network is trained using a synthetic dataset and we show how the classifier is able to generalize to different data, obtaining reliable estimations not only on synthetic data but also on real world scenes. Experimental results show that the proposed approach increases the accuracy of the depth estimation on both synthetic and real data and that it is able to outperform state-of-the-art methods. |
| Databáze: | OpenAIRE |
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