Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model
This work addresses depth estimation for robots using spherical cameras, but it is incremental as it builds on existing self-supervised frameworks with specific adaptations.
The paper tackled the problem of estimating 360-degree depth around a robot using monocular spherical camera images with unknown parameters, by proposing a learnable axisymmetric camera model and training with a photo-realistic simulator and floor constraints, resulting in demonstrated efficacy on datasets like GO Stanford and KITTI.
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.