Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMonocular Depth EstimationSelf-Supervised LearningSimilar Papers 제목 키워드 기반
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes
We present a generalised self-supervised learning approach for monocular estimation of the real depth across scenes with diverse depth ranges from 1--100s of meters. Existing supervised methods for monocular depth estima…
Depth EstimationMonocular Depth EstimationSelf-Supervised LearningSelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Monocular depth estimation in the wild inherently predicts depth up to an unknown scale. To resolve scale ambiguity issue, we present a learning algorithm that leverages monocular simultaneous localization and mapping (S…
Depth EstimationMonocular Depth EstimationRobot NavigationSelf-Supervised Learning+1FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume
Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame…
Depth EstimationMonocular Depth EstimationStereo Depth EstimationImage Masking for Robust Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation is a salient task for 3D scene understanding. Learned jointly with monocular ego-motion estimation, several methods have been proposed to predict accurate pixel-wise depth witho…
Autonomous DrivingDepth EstimationMonocular Depth EstimationMotion Estimation+1Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation
Monocular and binocular self-supervised depth estimations are two important and related tasks in computer vision, which aim to predict scene depths from single images and stereo image pairs respectively. In literature, t…
Depth EstimationMonocular Depth Estimation