Depth Estimation Using Single Camera with Dual Apertures

2017 ◽  
pp. 167-189
Author(s):  
Hyun Sang Park ◽  
Young-Gyu Kim ◽  
Yeongmin Lee ◽  
Woojin Yun ◽  
Jinyeon Lim ◽  
...  
Keyword(s):  
Depth Estimation ◽  
Single Camera

scholarly journals Depth Estimation of an Underwater Object Using a Single Camera

2017 ◽  
Vol 2 (2) ◽  
pp. 112 ◽  
Author(s):  
Benjamin Champion ◽  
Mo Jamshidi ◽  
Matthew Joordens
Keyword(s):  
Depth Estimation ◽  
Single Camera ◽  
Underwater Robotics ◽  
Complicated Problem ◽  
Underwater Object ◽  
Unknown Objects

<p>Underwater robotics is currently a growing field. To be able to autonomously find and collect objects on the land and in the air is a complicated problem, which is only compounded within the underwater setting. Different techniques have been developed over the years to attempt to solve this problem, many of which involve the use of expensive sensors. This paper explores a method to find the depth of an object within the underwater setting, using a single camera source and a known object. Once this known object has been found, information about other unknown objects surrounding this point can be determined, and therefore the objects can be collected.</p>

scholarly journals Learning Single Camera Depth Estimation Using Dual-Pixels

2019 ◽  
Author(s):  
Rahul Garg ◽  
Neal Wadhwa ◽  
Sameer Ansari ◽  
Jonathan Barron
Keyword(s):  
Depth Estimation ◽  
Single Camera

scholarly journals Depth Estimation of a Deformable Object via a Monocular Camera

2019 ◽  
Vol 9 (7) ◽  
pp. 1366 ◽  
Author(s):  
Guolai Jiang ◽  
Shaokun Jin ◽  
Yongsheng Ou ◽  
Shoujun Zhou
Keyword(s):  
Pose Estimation ◽  
A Priori ◽  
Depth Estimation ◽  
Target Object ◽  
Estimation Accuracy ◽  
Single Camera ◽  
Deformable Object ◽  
Nao Robot ◽  
Feature Based ◽  
Monocular Image

The depth estimation of the 3D deformable object has become increasingly crucial to various intelligent applications. In this paper, we propose a feature-based approach for accurate depth estimation of a deformable 3D object with a single camera, which reduces the problem of depth estimation to a pose estimation problem. The proposed method needs to reconstruct the target object at the very beginning. With the 3D reconstruction as an a priori model, only one monocular image is required afterwards to estimate the target object’s depth accurately, regardless of pose changes or deformability of the object. Experiments are taken on an NAO robot and a human to evaluate the depth estimation accuracy by the proposed method.

scholarly journals Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering

2021 ◽  
Vol 5 (3) ◽  
pp. 206
Author(s):  
Chuho Yi ◽  
Jungwon Cho
Keyword(s):  
Real Time ◽  
Autonomous Vehicle ◽  
Depth Map ◽  
Time Estimation ◽  
Weather Conditions ◽  
Depth Estimation ◽  
Normal Vector ◽  
Single Camera ◽  
Distance Information ◽  
Lower Accuracy

Estimating a road surface or planes for applying AR(Augmented Reality) or an autonomous vehicle using a camera requires significant computation. Vision sensors have lower accuracy in distance measurement than other types of sensor, and have the difficulty that additional algorithms for estimating data must be included. However, using a camera has the advantage of being able to extract various information such as weather conditions, sign information, and road markings that are difficult to measure with other sensors. Various methods differing in sensor type and configuration have been applied. Many of the existing studies had generally researched by performing the depth estimation after the feature extraction. However, recent studies have suggested using deep learning to skip multiple processes and use a single DNN(Deep Neural Network). Also, a method using a limited single camera instead of a method using a plurality of sensors has been proposed. This paper presents a single-camera method that performs quickly and efficiently by employing a DNN to extract distance information using a single camera, and proposes a modified method for using a depth map to obtain real-time surface characteristics. First, a DNN is used to estimate the depth map, and then for quick operation, normal vector that can connect similar planes to depth is calculated, and a clustering method that can be connected is provided. An experiment is used to show the validity of our method, and to evaluate the calculation time.

Real-time Depth Estimation and View Interpolation using Quasar

2016 ◽  
Vol 2016 (19) ◽  
pp. 1-6 ◽  
Author(s):  
Bart Goossens ◽  
Simon Donné ◽  
Jan Aelterman ◽  
Jonas De Vylder ◽  
Dirk Van Haerenborgh ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Estimation ◽  
View Interpolation

Muybridge / Technology

2010 ◽  
Vol 1 (1) ◽  
pp. 51-62
Author(s):  
Marta Braun
Keyword(s):  
Single Camera ◽  
Art And Science ◽  
Animal Locomotion ◽  
Human Figure ◽  
Final Work

Eadweard Muybridge's 1887 photographic atlas Animal Locomotion is a curious mixture of art and science, a polysemic text that has been subject to a number of readings. This paper focuses on Muybridge's technology. It seeks to understand his commitment to making photographs with a battery of cameras rather than a single camera. It suggests reasons for his choice of apparatus and shows how his final work, The Human Figure in Motion (1901), justifies the choices he made.

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