1 research outputs found
A Survey on Deep Learning Techniques for Stereo-based Depth Estimation
Estimating depth from RGB images is a long-standing ill-posed problem, which
has been explored for decades by the computer vision, graphics, and machine
learning communities. Among the existing techniques, stereo matching remains
one of the most widely used in the literature due to its strong connection to
the human binocular system. Traditionally, stereo-based depth estimation has
been addressed through matching hand-crafted features across multiple images.
Despite the extensive amount of research, these traditional techniques still
suffer in the presence of highly textured areas, large uniform regions, and
occlusions. Motivated by their growing success in solving various 2D and 3D
vision problems, deep learning for stereo-based depth estimation has attracted
growing interest from the community, with more than 150 papers published in
this area between 2014 and 2019. This new generation of methods has
demonstrated a significant leap in performance, enabling applications such as
autonomous driving and augmented reality. In this article, we provide a
comprehensive survey of this new and continuously growing field of research,
summarize the most commonly used pipelines, and discuss their benefits and
limitations. In retrospect of what has been achieved so far, we also conjecture
what the future may hold for deep learning-based stereo for depth estimation
research