VCIP2021-SDEDualENet

SDE-DualENet: A Novel Dual Efficient Convolutional Neural Network for Robust Stereo Depth Estimation

Rithvik Anil, Mansi Sharma, Rohit Choudhary

IEEE Visual Communications and Image Processing (IEEE VCIP 2021), Novotel Munich City, Germany

Abstract

Stereo depth estimation is dependent on optimal correspondence matching between pixels of stereo-pair image to infer depth. In this paper, we attempt to revisit the stereo depth estimation problem in a simple dual convolutional neural network (CNN) based on EfficientNet that avoids the construction of a cost volume in stereo matching. This has been performed by considering different weights in otherwise identical towers of the CNN. The proposed algorithm is dubbed as SDE-DualENet. The architecture of SDE-DualENet eliminates the construction of cost-volume by learning to match correspondence between pixels with a different set of weights in the dual towers. The results are demonstrated on complex scenes with high details and large depth variations. The SDE-DualENet depth prediction network outperforms state-of-the-art monocular and stereo depth estimation methods, both qualitatively and quantitatively on challenging scene flow dataset. The code and pre-trained models will be made publicly available.

VCIP_poster.pdf

VCIP2021CR.pdf

Citation

R. Anil, M. Sharma and R. Choudhary, "SDE-DualENet: A Novel Dual Efficient Convolutional Neural Network for Robust Stereo Depth Estimation," 2021 International Conference on Visual Communications and Image Processing (VCIP), 2021, pp. 1-5, doi: 10.1109/VCIP53242.2021.9675391.