HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

Understand

Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360{\deg} surroundings.

• Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition.
• In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation.
• Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges.