  Display technologies have evolved over the years. It is critical to develop
practical HDR capturing, processing, and display solutions to bring 3D
technologies to the next level. Depth estimation of multi-exposure stereo image
sequences is an essential task in the development of cost-effective 3D HDR
video content. In this paper, we develop a novel deep architecture for
multi-exposure stereo depth estimation. The proposed architecture has two novel
components. First, the stereo matching technique used in traditional stereo
depth estimation is revamped. For the stereo depth estimation component of our
architecture, a mono-to-stereo transfer learning approach is deployed. The
proposed formulation circumvents the cost volume construction requirement,
which is replaced by a ResNet based dual-encoder single-decoder CNN with
different weights for feature fusion. EfficientNet based blocks are used to
learn the disparity. Secondly, we combine disparity maps obtained from the
stereo images at different exposure levels using a robust disparity feature
fusion approach. The disparity maps obtained at different exposures are merged
using weight maps calculated for different quality measures. The final
predicted disparity map obtained is more robust and retains best features that
preserve the depth discontinuities. The proposed CNN offers flexibility to
train using standard dynamic range stereo data or with multi-exposure low
dynamic range stereo sequences. In terms of performance, the proposed model
surpasses state-of-the-art monocular and stereo depth estimation methods, both
quantitatively and qualitatively, on challenging Scene flow and differently
exposed Middlebury stereo datasets. The architecture performs exceedingly well
on complex natural scenes, demonstrating its usefulness for diverse 3D HDR
applications.
