On the synergies between machine learning and binocular stereo for depth estimation from images: a survey
Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …
years of studies and research. Throughout the years the paradigm has shifted from local …
Digging into self-supervised monocular depth estimation
Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this
limitation, self-supervised learning has emerged as a promising alternative for training …
limitation, self-supervised learning has emerged as a promising alternative for training …
Self-supervised monocular depth hints
Monocular depth estimators can be trained with various forms of self-supervision from
binocular-stereo data to circumvent the need for high-quality laser-scans or other ground …
binocular-stereo data to circumvent the need for high-quality laser-scans or other ground …
Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
A Johnston, G Carneiro - … of the ieee/cvf conference on …, 2020 - openaccess.thecvf.com
Monocular depth estimation has become one of the most studied applications in computer
vision, where the most accurate approaches are based on fully supervised learning models …
vision, where the most accurate approaches are based on fully supervised learning models …
Towards real-time monocular depth estimation for robotics: A survey
As an essential component for many autonomous driving and robotic activities such as ego-
motion estimation, obstacle avoidance and scene understanding, monocular depth …
motion estimation, obstacle avoidance and scene understanding, monocular depth …
The edge of depth: Explicit constraints between segmentation and depth
In this work we study the mutual benefits of two common computer vision tasks, self-
supervised depth estimation and semantic segmentation from images. For example, to help …
supervised depth estimation and semantic segmentation from images. For example, to help …
Self-supervised monocular depth estimation with multiscale perception
Extracting 3D information from a single optical image is very attractive. Recently emerging
self-supervised methods can learn depth representations without using ground truth depth …
self-supervised methods can learn depth representations without using ground truth depth …
Pseudo rgb-d for self-improving monocular slam and depth prediction
Abstract Classical monocular Simultaneous Localization And Mapping (SLAM) and the
recently emerging convolutional neural networks (CNNs) for monocular depth prediction …
recently emerging convolutional neural networks (CNNs) for monocular depth prediction …
On deep learning techniques to boost monocular depth estimation for autonomous navigation
R de Queiroz Mendes, EG Ribeiro… - Robotics and …, 2021 - Elsevier
Inferring the depth of images is a fundamental inverse problem within the field of Computer
Vision since depth information is obtained through 2D images, which can be generated from …
Vision since depth information is obtained through 2D images, which can be generated from …
Sharingan: Combining synthetic and real data for unsupervised geometry estimation
We propose a novel method for combining synthetic and real images when training
networks to determine geometric information from a single image. We suggest a method for …
networks to determine geometric information from a single image. We suggest a method for …