Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences

• URL: http://arxiv.org/abs/2005.14313v1
• Date: Thu, 28 May 2020 21:53:53 GMT
• Title: Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences
• Authors: Alwyn Mathew, Aditya Prakash Patra, Jimson Mathew
• Abstract summary: LiDAR and radar sensors are the hardware solution for real-time depth estimation. Deep learning based self-supervised depth estimation methods have shown promising results. We propose a self-attention based depth and ego-motion network for unrectified images.
• Score: 6.821598757786515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The dense depth estimation of a 3D scene has numerous applications, mainly in robotics and surveillance. LiDAR and radar sensors are the hardware solution for real-time depth estimation, but these sensors produce sparse depth maps and are sometimes unreliable. In recent years research aimed at tackling depth estimation using single 2D image has received a lot of attention. The deep learning based self-supervised depth estimation methods from the rectified stereo and monocular video frames have shown promising results. We propose a self-attention based depth and ego-motion network for unrectified images. We also introduce non-differentiable distortion of the camera into the training pipeline. Our approach performs competitively when compared to other established approaches that used rectified images for depth estimation.

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