  In this project, we propose a novel approach for estimating depth from RGB
images. Traditionally, most work uses a single RGB image to estimate depth,
which is inherently difficult and generally results in poor performance, even
with thousands of data examples. In this work, we alternatively use multiple
RGB images that were captured while changing the focus of the camera's lens.
This method leverages the natural depth information correlated to the different
patterns of clarity/blur in the sequence of focal images, which helps
distinguish objects at different depths. Since no such data set exists for
learning this mapping, we collect our own data set using customized hardware.
We then use a convolutional neural network for learning the depth from the
stacked focal images. Comparative studies were conducted on both a standard
RGBD data set and our own data set (learning from both single and multiple
images), and results verified that stacked focal images yield better depth
estimation than using just single RGB image.
