Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model

TL;DR

This paper presents a self-supervised approach for 360-degree monocular depth estimation using a learnable camera model and synthetic training data, enabling depth estimation without ground truth camera parameters.

Contribution

It introduces a learnable axisymmetric camera model for spherical images and trains with synthetic data to estimate depth around a robot without known camera parameters.

Findings

Effective depth estimation on spherical camera images

Improved camera parameter learning compared to baselines

Reduced artifacts from reflective surfaces

Abstract

Self-supervised monocular depth estimation has been widely investigated to estimate depth images and relative poses from RGB images. This framework is attractive for researchers because the depth and pose networks can be trained from just time sequence images without the need for the ground truth depth and poses. In this work, we estimate the depth around a robot (360 degree view) using time sequence spherical camera images, from a camera whose parameters are unknown. We propose a learnable axisymmetric camera model which accepts distorted spherical camera images with two fisheye camera images. In addition, we trained our models with a photo-realistic simulator to generate ground truth depth images to provide supervision. Moreover, we introduced loss functions to provide floor constraints to reduce artifacts that can result from reflective floor surfaces. We demonstrate the efficacy of our method using the spherical camera images from the GO Stanford dataset and pinhole camera images from the KITTI dataset to compare our method's performance with that of baseline method in learning the camera parameters.