Generation of Depth Map Based on Depth from Focus: A Survey

Kulkarni, Jyoti B.; SheelaRani, C. M.

doi:10.1109/iccubea.2015.146

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…A good survey on methods using depth from defocus can be found in ref. [5]. Let us remind that depth from defocus technique is fundamentally different from depth from focus in the sense that the later uses a stack of images to model the blur in image while the former technique uses a single image.…”

Section: Depth Perceptionmentioning

confidence: 99%

Depth Learning Methods For Bridges Inspection Using UAV

Sekkati,

Lapointe

2023

Drones - Various Applications

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This paper is investigating learning methods using depth as a cue measurement that can be used for bridge inspection. We investigate learning methods based on mono, stereo, and multiview image input and discuss the constraints that allow some methods to perform better than others in various scenarios. We go over the state-of-the-art deep learning methods, including supervised and unsupervised methods. These methods will be compared and evaluated, based on constraints, performance, and accuracy, and how top methods should be selected for each scenario. The same database should be used for fair comparison between all methods ensuring that evaluations are unbiased, replicable, and meaningful.

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Section: Depth Perceptionmentioning

confidence: 99%

Depth Learning Methods For Bridges Inspection Using UAV

Sekkati,

Lapointe

2023

Drones - Various Applications

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“…The existing depth map generation algorithms are mainly classified in two categories: Automatic method and Semi-Automatic method. In Automatic method, different depth cues are considered such as focus and defocus information [9, 10] where image’s focus data is considered by varying the focus parameters of a camera. Yang et al .…”

Section: Related Workmentioning

confidence: 99%

Degraded image enhancement by image dehazing and Directional Filter Banks using Depth Image based Rendering for future free-view 3D-TV

et al. 2019

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DIBR-3D technology has evolved over the past few years with the demands of consumers increasing in recent times for future free-view 3D videos on their home televisions. The main issue in 3D technology is the lack of 3D content available to watch using the traditional TV systems. Although, some sophisticated devices like stereoscopic cameras have been used to fill the gap between the 3D content demand and 3D content supply. But the content generated through these sophisticated devices can not be displayed on the traditional TV systems, so there needs to be some mechanism which is inline with the traditional TV. Furthermore, the huge collection of existing 2D content should be converted to 3D using depth image-based rendering techniques. This conversion technique can highly contribute in overcoming the shortage problem of the 3D content. This paper presents a novel approach for converting 2D degraded image for DIBR 3D-TV view. This degraded or noisy/blur image is enhanced through image dehazing and Directional Filter Bank (DFB). This enhancement is necessary because of the occlusion effect or hole filling problem that occurs due to imperfect depth map. The enhanced image is then segmented into the foreground image and the background image. After the segmentation, the depth map is generated using image profiles. Moreover, Stereoscopic images are finally produced using the DIBR procedure which is based on the 2D input image and the corresponding depth map. We have verified the results of the proposed approach by comparing the results with the existing state-of-the-art techniques.

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“…From how the shades form on the object under different angles, its 3D form can be calculated. A single camera can also be used for 3D imaging with the focus technique, where multiple images are taken from the object of interest with various focusing distances [3]. For each pixel, the focusing distance, when it appears to be the sharpest, is considered as its distance to the sensor.…”

Section: Introductionmentioning

confidence: 99%

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

et al. 2020

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The fast evolution in computational and sensor technologies brings previously niche solutions to a wider userbase. As such, 3D reconstruction technologies are reaching new use-cases in scientific and everyday areas where they were not present before. Cost-effective and easy-to-use solutions include camera-based 3D scanning techniques, such as photogrammetry. This paper provides an overview of the available solutions and discusses in detail the depth-image based Real-time Appearance-based Mapping (RTAB-Map) technique as well as a smartphone-based solution that utilises ARCore, the Augmented Reality (AR) framework of Google. To qualitatively compare the two 3D reconstruction technologies, a simple length measurement-based method was applied with a purpose-designed reference object. The captured data were then analysed by a processing algorithm. In addition to the experimental results, specific case studies are briefly discussed, evaluating the applicability based on the capabilities of the technologies. As such, the paper presents the use-case of interior surveying in an automated laboratory as well as an example for using the discussed techniques for landmark surveying. The major findings are that point clouds created with these technologies provide a direction- and shape-accurate model, but those contain mesh continuity errors, and the estimated scale factor has a large standard deviation.

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Generation of Depth Map Based on Depth from Focus: A Survey

Cited by 6 publications

References 7 publications

Depth Learning Methods For Bridges Inspection Using UAV

Depth Learning Methods For Bridges Inspection Using UAV

Degraded image enhancement by image dehazing and Directional Filter Banks using Depth Image based Rendering for future free-view 3D-TV

A Benchmark of Popular Indoor 3D Reconstruction Technologies: Comparison of ARCore and RTAB-Map

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