Depth Estimation has wide reaching applications in the field of Computer vision such as target tracking, augmented reality, and self-driving cars. The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input. The traditional depth estimation methods are based on depth cues and used concepts like epipolar geometry. With the evolution of Convolutional Neural Networks, depth estimation has undergone tremendous strides. In this project, our aim is to explore possible extensions to existing SoTA Deep Learning based Depth Estimation Models and to see whether performance metrics could be further improved. In a broader sense, we are looking at the possibility of implementing Pose Estimation, Efficient Sub-Pixel Convolution Interpolation, Semantic Segmentation Estimation techniques to further enhance our proposed architecture and to provide fine-grained and more globally coherent depth map predictions. We also plan to do away with camera intrinsic parameters during training and apply weather augmentations to further generalize our model.

翻译：深度估计在计算机视觉领域具有广泛的应用，例如目标跟踪、增强现实和自动驾驶。单目深度估计的目标是根据输入的二维单目RGB图像预测深度图。传统的深度估计方法依赖深度线索，并采用对极几何等概念。随着卷积神经网络的演变，深度估计取得了巨大进展。在本项目中，我们的目标是探索现有基于深度学习的最先进深度估计模型的潜在扩展，并研究性能指标是否能进一步提升。从更广泛的意义上讲，我们正在考虑实现姿态估计、高效亚像素卷积插值和语义分割估计技术，以进一步优化我们提出的架构，并提供更精细且全局一致的深度图预测。同时，我们计划在训练过程中省略相机内参，并应用天气增强手段，以进一步提升模型的泛化能力。