Environment reconstruction on disparity images using surface features and Generative Adversarial Networks - Matias, Lucas Peres Nunes (2020)
The study and development of autonomous vehicles have become more relevant at each day. For the intelligent vehicle to be able to navigate through a real urban environment it is necessary a high degree of reliability to ensure the passenger and pedestrians safety. Therefore, sensors and algorithms used to help the decision making during the autonomous navigation need the maximum amount of information available, for the environment analysis to be most complete as possible. As an human driver, the computer should analyze the surrounding environment and evaluate the possible actions to execute in order to reach the final destination safely. However, despite the high precision data collected by the sensors, computational methods has a disadvantage when compared to the human cognition. A human driver can analyze the surrounding environment and deduce occluded information, more specifically information related to the environment behind objects and structures. For computational methods extract this missing data is a challenge. Recent works on image processing propose methods to estimate the area behind specified regions. Yet, those methods are applied on RGB images, where the focus is a visually satisfactory result. When dealing with disparity images, which codify depth data, it is necessary a coherent and precise estimation, since any noise on the image will be intensified in the tridimensional reconstruction, and will influence on the decision making algorithms environment interpretation. In this work we deal with the hypothesis of, by using specific disparity and depth features as guideline for the disparity image estimation, it is possible to achieve a coherent environment reconstruction of the area behind a masked region. Our results point out to this hypothesis validation, since we achieve - at the end of this work - a continuous environment reconstruction without significant noise.