Self-supervised monocular image depth learning and confidence estimation
نویسندگان
چکیده
منابع مشابه
Self-Supervised Monocular Image Depth Learning and Confidence Estimation
Convolutional Neural Networks (CNNs) need large amounts of data with ground truth annotation, which is a challenging problem that has limited the development and fast deployment of CNNs for many computer vision tasks. We propose a novel framework for depth estimation from monocular images with corresponding confidence in a selfsupervised manner. A fully differential patch-based cost function is...
متن کاملSelf-Supervised Siamese Learning on Stereo Image Pairs for Depth Estimation in Robotic Surgery
INTRODUCTION Robotic surgery has become a powerful tool for performing minimally invasive procedures, providing advantages in dexterity, precision, and 3D vision, over traditional surgery. One popular robotic system is the da Vinci surgical platform, which allows preoperative information to be incorporated into live procedures using Augmented Reality (AR). Scene depth estimation is a prerequisi...
متن کاملDepth Estimation of A Monocular Image from Image Blur
In computer vision, estimating the depth information of object from an image/video has always been a hot topic, and finds many applications. In this paper, we make use of the image blurs caused by defocus aberration to estimate the relative distance in an object. To be more computationally efficient, we focus on the blurs along image edges. Experiments shows the effectiveness of our proposed me...
متن کاملFusion of stereo and still monocular depth estimates in a self-supervised learning context
We study how autonomous robots can learn by themselves to improve their depth estimation capability. In particular, we investigate a self-supervised learning setup in which stereo vision depth estimates serve as targets for a convolutional neural network (CNN) that transforms a single still image to a dense depth map. After training, the stereo and mono estimates are fused with a novel fusion m...
متن کاملMonocular Depth Estimation: Applications to Image Segmentation and Filtering
This Ph.D. dissertation addresses the problem of estimating depth ordering information from single images, a key issue in image understanding that in recent years has focused the interest of the community. Motivation behind this tendency is provided by several important applications that could beneficiate of advances in the field such as automatic object removal, image indexing, 3D scene recons...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Neurocomputing
سال: 2020
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2019.11.038