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 …
Monovit: Self-supervised monocular depth estimation with a vision transformer
Self-supervised monocular depth estimation is an attractive solution that does not require
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have …
Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance
M Klingner, JA Termöhlen, J Mikolajczyk… - Computer Vision–ECCV …, 2020 - Springer
Self-supervised monocular depth estimation presents a powerful method to obtain 3D scene
information from single camera images, which is trainable on arbitrary image sequences …
information from single camera images, which is trainable on arbitrary image sequences …
Feature-metric loss for self-supervised learning of depth and egomotion
C Shu, K Yu, Z Duan, K Yang - European Conference on Computer Vision, 2020 - Springer
Photometric loss is widely used for self-supervised depth and egomotion estimation.
However, the loss landscapes induced by photometric differences are often problematic for …
However, the loss landscapes induced by photometric differences are often problematic for …
Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
We present a novel method for simultaneous learning of depth, egomotion, object motion,
and camera intrinsics from monocular videos, using only consistency across neighboring …
and camera intrinsics from monocular videos, using only consistency across neighboring …
On the uncertainty of self-supervised monocular depth estimation
Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …
not require ground truth annotations at all. Despite the astonishing results yielded by such …
Every pixel counts++: Joint learning of geometry and motion with 3d holistic understanding
Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames
by watching unlabeled videos via deep convolutional network has made significant progress …
by watching unlabeled videos via deep convolutional network has made significant progress …
Unsupervised monocular depth learning in dynamic scenes
We present a method for jointly training the estimation of depth, ego-motion, and a dense 3D
translation field of objects relative to the scene, with monocular photometric consistency …
translation field of objects relative to the scene, with monocular photometric consistency …
Unsupervised adversarial depth estimation using cycled generative networks
While recent deep monocular depth estimation approaches based on supervised regression
have achieved remarkable performance, costly ground truth annotations are required during …
have achieved remarkable performance, costly ground truth annotations are required during …
Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos
In this paper, we propose UnOS, an unified system for unsupervised optical flow and stereo
depth estimation using convolutional neural network (CNN) by taking advantages of their …
depth estimation using convolutional neural network (CNN) by taking advantages of their …