Innovative Stereo Vision-Based Approach to Generate Dense Depth Map of Transportation Infrastructure

2011 ◽  
Vol 2215 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Abbas Rashidi ◽  
Habib Fathi ◽  
Ioannis Brilakis
Keyword(s):  
Stereo Vision ◽  
Depth Map ◽  
Transportation Infrastructure

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

scholarly journals RGB–D terrain perception and dense mapping for legged robots

2016 ◽  
Vol 26 (1) ◽  
pp. 81-97 ◽  
Author(s):  
Dominik Belter ◽  
Przemysław Łabecki ◽  
Péter Fankhauser ◽  
Roland Siegwart
Keyword(s):  
Stereo Vision ◽  
Uncertainty Propagation ◽  
Depth Map ◽  
Mapping Method ◽  
Spatial Uncertainty ◽  
Legged Robots ◽  
Walking Robots ◽  
Terrain Mapping ◽  
Terrain Elevation ◽  
Uncertainty Models

Abstract This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.

scholarly journals 3D Stereoscopy Video Production: Z-Depth Extraction and Optimized Rendering using Foundry Nuke

2019 ◽  
Vol 9 (1) ◽  
pp. 6458-6664
Keyword(s):  
Case Studies ◽  
Stereo Vision ◽  
State Of The Art ◽  
Depth Map ◽  
Production Quality ◽  
Theoretical Explanation ◽  
Video Production ◽  
Stereo Video ◽  
Depth Extraction ◽  
Technical Issues

This paper proposed an effective and efficient 3D stereo Video production methodology for Stereo conversion of any image or video using the 3D compositing tool Foundry Nuke. For efficient S3D conversion, there are several pipelines uses by video production engineer into the industry. There are several technical issues during the 3D stereo production like spilling, reflection, translucency, occlusion, flickering and noise problems as well due to imperfect calibration. This paper presents a theoretical explanation of the principles of stereo vision systems, followed by a quick review of the state of the art. The research paper concludes with validating the assumption of 3D stereoscopy video conversion with film case studies to execute high-end 3d stereo video production quality with roto-based depth map extraction with optimized render conversion methodology.

scholarly journals Pavement distress detection by stereo vision / Straßenzustandserkennung durch stereoskopische Bildverarbeitung

2019 ◽  
Vol 86 (s1) ◽  
pp. 42-46
Author(s):  
Hauke Brunken ◽  
Clemens Gühmann
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Depth Map ◽  
Color Images ◽  
Second Step ◽  
Road Maintenance ◽  
Moving Vehicle ◽  
Pavement Distress ◽  
The Road ◽  
Driving Speed

AbstractFor road maintenance up-to-date information about road conditions is important. Such information is currently expensive to obtain. Specially equipped measuring vehicles have to perform surface scans of the road, and it is unclear how to automatically Ąnd faulty sections in these scans. This research solves the problem by stereo vision with cameras mounted behind the windshield of a moving vehicle so that the system can easily be integrated into a large number of vehicles. The stereo images are processed into a depth map of the road surface. In a second step, color images from the cameras are combined with the depth map and are classified by a convolutional neural network. It is shown that the developed system is able to Ąnd defects that require knowledge about surface deformations. These defects could not have been found on monocular images. The images are taken at usual driving speed.

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