Self-Supervised Learning based Depth Estimation from Monocular Images
- URL: http://arxiv.org/abs/2304.06966v1
- Date: Fri, 14 Apr 2023 07:14:08 GMT
- Title: Self-Supervised Learning based Depth Estimation from Monocular Images
- Authors: Mayank Poddar, Akash Mishra, Mohit Kewlani and Haoyang Pei
- Abstract summary: The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input.
We plan to do intrinsic camera parameters during training and apply weather augmentations to further generalize our model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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