  Without using extra 3-D data like points cloud or depth images for providing
3-D information, we retrieve the 3-D object information from single monocular
images. The high-quality predicted depth images are recovered from single
monocular images, and it is fed into the 2-D object proposal network with
corresponding monocular images. Most existing deep learning frameworks with
two-streams input data always fuse separate data by concatenating or adding,
which views every part of a feature map can contribute equally to the whole
task. However, when data are noisy, and too much information is redundant,
these methods no longer produce predictions or classifications efficiently. In
this report, we propose a two-dimensional CCA(canonical correlation analysis)
framework to fuse monocular images and corresponding predicted depth images for
basic computer vision tasks like image classification and object detection.
Firstly, we implemented different structures with one-dimensional CCA and
Alexnet to test the performance on the image classification task. And then, we
applied one of these structures with 2D-CCA for object detection. During these
experiments, we found that our proposed framework behaves better when taking
predicted depth images as inputs with the model trained from ground truth
depth.
