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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 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 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. [ABSTRACT FROM AUTHOR]

We propose an encoder–decoder CNN framework to predict depth from one single image in a self-supervised manner. To this aim, we design three kinds of encoder based on the recent advanced deep neural network and one kind of decoder which can generate multiscale predictions. Eight loss functions are designed based on the proposed encoder–decoder CNN framework to validate the performance. For training, we take rectified stereo image pairs as input of the CNN, which is trained by reconstructing image via learning multiscale disparity maps. For testing, the CNN can estimate the accurate depth information by inputting only one single image. We validate our framework on two public datasets in contrast to the state-of-the-art methods and our designed different variants, and the performance of different encoder–decoder architectures and loss functions is evaluated to obtain the best combination, which proves that our proposed method performs very well for single-image depth estimation without the supervision of ground truth. [ABSTRACT FROM AUTHOR]