An Image-Based Relighting Method

A technology of re-lighting and images, applied in the field of machine learning and graphics, can solve the problems of increasing work intensity and storage space, and achieve the effect of less image samples, less training time, and high PSNR value

Inactive Publication Date: 2019-06-21
HOHAI UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] IBR often needs to obtain image samples through dense sampling, which greatly increases the work intensity and storage space

Method used

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Embodiment Construction

[0028] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0029] An image-based re-illumination method of the present invention, such as figure 1 shown, including:

[0030] Step 1: Collect a set of three-dimensional scene data (Dagon, Mitsuba), including the LigX and LigY coordinates of the point light source and the corresponding image set ImageSet output at a fixed viewpoint; calculate the average of the image set in the three channels of R, G, and B Value, get ImgAvg_R, ImgAvg_G, ImgAvg_B; scene data is shown in Table 1.

[0031] Table 1 scene data

[0032] Scenes Light source distribution image size dragon 31×31 64×48 Mitsuba 21×21 64×48

[0033] Step 2: Randomly sample in the image set ImageSet to form the image subset ImageSubset, and the number of image samples is ImageNum.

[0034] Step 3: Randomly sample the pixels of the image subset like ImageSubset to obtain...

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Abstract

The invention discloses an image-based re-illumination method, which belongs to the field of computer graphics. In order to achieve as accurate relighting as possible with as few samples as possible, quantitative random sampling is repeatedly performed in the two spaces of image samples and image pixels, and artificial neural network is used for training until the training accuracy of all pixels reaches the set threshold. Considering that the artificial neural network has a minimum sample requirement during training, when the pixel training samples are insufficient, the Bagging algorithm idea of ​​integrated learning is used to average them. The present invention is tested in a simulated three-dimensional scene, and the results show that compared with the existing technology, not only the training time is less, but also the robustness is strong; under the same relative error accuracy, the image samples required for re-illumination are smaller, and the speed The fast real-time performance is good, and the PSNR value of the reconstructed scene image is higher.

Description

technical field [0001] The invention relates to an image-based re-illumination method, belonging to the fields of machine learning and graphics. Background technique [0002] Image-based Relighting (Image-based Relighting, IBR), also known as Image-based Rendering, its purpose is to start from the captured image, calculate the light transmission matrix and draw the new light source conditions scene image. Its biggest advantage is that it does not require the geometric information of the scene, the rendering is not affected by the complexity of the scene, and it can also express various lighting effects such as reflection, refraction, and scattering. Therefore, since IBR was proposed, it immediately became the focus of attention in the field of graphics. [0003] IBR often needs to obtain image samples through dense sampling, which greatly increases the work intensity and storage space. Whether the machine learning method can be used to achieve image-based re-illumination ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T15/20G06N3/04G06N3/08
CPCG06N3/08G06T15/205G06N3/045
Inventor 韦伟刘惠义钱苏斌
Owner HOHAI UNIV
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