Reflectance Modeling in Machine Vision: Applications in Image Analysis and Synthesis

Gruna, Robin; Irgenfried, Stephan

doi:10.5772/26554

Cited by 2 publications

(2 citation statements)

References 66 publications

(65 reference statements)

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“…In the computer vision literature, synthetic images are utilized in a wide range of use cases. They are for instance used in the training of algorithms [2], [3], [4], analysis of uncertainties [5], evaluation of algorithms [1], [6], [7], analysis of visual features [8], [9] and offline programming and prototyping [10], [11]. The methods used for generating the images, the realism of the images and the methods of evaluation differs greatly depending on the use case.…”

Section: Related Workmentioning

confidence: 99%

Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

Iversen

Wilm

Kraft

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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A growing trend in computer vision is the use of synthetic images for the evaluation of computer vision algorithms such as 3D pose estimation. This is partly due to the availability of high-quality render engines, which provide highly realistic synthetic images. However, the realism of the rendered images, and thus the reliability of the evaluations, strongly depends on how accurately the scenes are modeled and it requires considerable time and knowledge to do the modeling manually. Automating the modeling process is therefore crucial for making the rendering of photo-realistic synthetic images accessible to the wider robotics community.We present a method for automatically modeling object and light properties for rigid, opaque plastic objects commonly found in industry. Our method relies on recordings of the environment captured with a consumer 360 • camera to model the light, and on analysis-by-synthesis to estimate the optical properties of the objects. We show that the synthetic images rendered based on our automatic modeling method can be used to predict the overall performance of a monocular 3D pose estimation algorithm.

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Section: Related Workmentioning

confidence: 99%

Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

Iversen

Wilm

Kraft

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…More recently, Gruna and Irgenfried [GI12] developed a vision simulator to generate photo realistic images of a given scene for varying lighting conditions. This simulator was used to generate various images to be used for comparing image processing algorithms.…”

Section: Related Workmentioning

confidence: 99%

Using physically Based Rendering to Benchmark Structured Light Scanners

Medeiros

Doraiswamy

Berger

et al. 2014

Computer Graphics Forum

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Structured light scanning is ubiquituous in 3D acquisition. It is capable of capturing high geometric detail at a low cost under a variety of challenging scene conditions. Recent methods have demonstrated robustness in the presence of artifacts due to global illumination, such as inter-reflections and sub-surface scattering, as well as imperfections caused by projector defocus. For comparing approaches, however, the quantitative evaluation of structured lighting schemes is hindered by the challenges in obtaining ground truth data, resulting in a poor understanding for these methods across a wide range of shapes, materials, and lighting configurations. In this paper, we present a benchmark to study the performance of structured lighting algorithms in the presence of errors caused due to the above properties of the scene. In order to do this, we construct a synthetic structured lighting scanner that uses advanced physically based rendering techniques to simulate the point cloud acquisition process. We show that, under conditions similar to that of a real scanner, our synthetic scanner replicates the same artifacts found in the output of a real scanner. Using this synthetic scanner, we perform a quantitative evaluation of four different structured lighting techniques -gray-code patterns, micro-phase shifting, ensemble codes, and unstructured light scanning. The evaluation, performed on a variety of scenes,demonstrate that no one method is capable of adequately handling all sources of error -each method is appropriate for addressing distinct sources of error.

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Reflectance Modeling in Machine Vision: Applications in Image Analysis and Synthesis

Cited by 2 publications

References 66 publications

Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

Rapid Estimation of Optical Properties for Simulation-Based Evaluation of Pose Estimation Performance

Using physically Based Rendering to Benchmark Structured Light Scanners

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