• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.75-83
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• 2019

Direct volume rendering is a widely used method for visualizing three-dimensional volume data such as medical images. This paper proposes a method for applying depth-of-field effects to volume ray-casting to enable more realistic depth-of-filed rendering in direct volume rendering. The proposed method exploits a camera model based on the human perceptual model and can obtain realistic images with a limited number of rays using jittered lens sampling. It also enables interactive exploration of volume data by on-the-fly calculating depth-of-field in the GPU pipeline without preprocessing. In the experiment with various data including medical images, we demonstrated that depth-of-field images with better depth perception were generated 2.6 to 4 times faster than the conventional method.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.1-10
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• 2018

In this paper, we present a non-photorealistic rendering technique that automatically delivers a stylized abstraction of a photograph with DoF(Depth of field). Our approach is a new filtering method that efficiently classifies DoF regions using RGB channels and automatically adjusts the color abstraction and extracted line quality based on this classification. This DoF-based filtering is simple, fast, and easy to implement and significantly improves the abstraction performance in terms of feature enhancement and stylization.

• Journal of Korea Game Society
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• v.20 no.6
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• pp.53-62
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• 2020

This paper proposes on the implementation method of depth of field effect based on backward mapping method available in real-time rendering enviroment using calculation of sampling range based on standard normal distribution and alpha blending of color of layers. To implement the effect, this paper describe how to calculate radius of circle of confusion, establish sampling radius using circle of confusion, and determine color through alpha blending of the multiple layer and denoising.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.33-38
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• 2008

본 논문은 핀홀 카메라 모델에 의해 렌더링한 컬러와 깊이 이미지의 후처리에 의한 실시간 필드심도 렌더링 방법을 제안한다. 필드심도 렌더링은 최근의 향상에도, 큰 스케일의 블러링에 필요한 계산 때문에 실용적으로 사용되지 못해 왔다. 본 방법은 이방성 가우시안 필터로 생성된 밉맵 이미지들을 비선형으로 보간하여 필드심도 효과에 필요한 블러링을 수행한다. 모든 계산 과정은 GPU로 가속되어, 안정적이고 확장 가능한 실시간 수행 성능을 확보한다. 또한, 후처리 방식의 두 가지 결점인 강도 누출과 블러링 불연속성을 이방성 가우시안 필터와 블러링 정도를 부드럽게 하여 제거한다. 본 방법은 뛰어난 실시간 성능과 함께 고품질의 필드심도 효과를 생성한다.

• Journal of the Korea Computer Graphics Society
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• v.6 no.4
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• pp.15-22
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• 2000

One of the main research trends in image based modeling and rendering is how to implement plenoptic function. For this purpose, this paper proposes a novel approach based on a set of randomly placed panoramas. The proposed approach, first of all, adopts a simple computer vision technique to approximate omni-directional depth information of the surrounding scene, and then corrects/interpolates panorama images to generate an output image at a vantage viewpoint. Implementation results show that the proposed approach achieves smooth navigation at an interactive rate.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.253-256
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• 2021

본 논문에서는 합성곱 신경망을 통해 학습된 DoF(피사계 심도, Depth of field) 네트워크 아키텍처를 이용하여 객체 인식, 시점 추적, 문자 인식, 비사실적 렌더링 등 다양한 애플리케이션에 적용할 수 있는 사후 필터링 기법에 대해 살펴본다. 일반적으로 영상은 포커싱과 아웃포커싱에 의해 사용자의 관심표현이 결정되며, 이를 이용하여 영상 내 중요도를 판단한다. 영상 내에는 수많은 콘텐츠들이 혼재되어 있기 때문에 사용자가 집중적으로 보고 있는 콘텐츠를 찾아내기 어렵다. 본 논문에서는 사용자가 흥미롭고 집중적으로 보고 있는 영역을 DoF 네트워크로 학습시키고, 이를 통해 이전 기법으로는 표현할 수 없었던 DoF 기반 객체 인식, 시점 추적, 문자 인식, 비사실적 렌더링을 효율적으로 표현해낸다.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.164-177
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• 2009

The DIBR(depth image-based rendering) method gives the sense of depth to viewers by using one color image and corresponding depth image. At this time, the qualities of the generated left- and right-image depend on the baseline distance of the virtual cameras corresponding to the view of the generated left- and right-image. In this paper, we present a novel method for enhancing the sense of depth by adjusting baseline distance of virtual cameras. Geometric analysis shows that the sense of depth is better in accordance with the increasing disparity due to the reduction of the image distortion. However, the entailed image degradation is not considered. Experimental results show that there is maximum bound in the disparity increasement due to image degradation and the visual field. Since the image degradation is reduced for increasing that bound, we add a depth map preprocessing. Since the interactive service where the disparity and view position are controlled by viewers can also be provided, the proposed method can be applied to the mobile broadcasting system such as DMB as well as 3DTV system.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.51-57
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• 2021

In this paper, we propose a method to find the DoF(Depth of field) that is blurred in an image by focusing and out-focusing the camera through a efficient convolutional neural network. Our approach uses the RGB channel-based cross-correlation filter to efficiently classify the DoF region from the image and build data for learning in the convolutional neural network. A data pair of the training data is established between the image and the DoF weighted map. Data used for learning uses DoF weight maps extracted by cross-correlation filters, and uses the result of applying the smoothing process to increase the convergence rate in the network learning stage. The DoF weighted image obtained as the test result stably finds the DoF region in the input image. As a result, the proposed method can be used in various places such as NPR(Non-photorealistic rendering) rendering and object detection by using the DoF area as the user's ROI(Region of interest).

• ETRI Journal
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• v.44 no.1
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• pp.24-37
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• 2022

Virtual view synthesis, which generates novel views similar to the characteristics of actually acquired images, is an essential technical component for delivering an immersive video with realistic binocular disparity and smooth motion parallax. This is typically achieved in sequence by warping the given images to the designated viewing position, blending warped images, and filling the remaining holes. When considering 6DoF use cases with huge motion, the warping method in patch unit is more preferable than other conventional methods running in pixel unit. Regarding the prior case, the quality of synthesized image is highly relevant to the means of blending. Based on such aspect, we proposed a novel blending architecture that exploits the similarity of the directions of rays and the distribution of depth values. By further employing the proposed method, results showed that more enhanced view was synthesized compared with the well-designed synthesizers used within moving picture expert group (MPEG-I). Moreover, we explained the GPU-based implementation synthesizing and rendering views in the level of real time by considering the applicability for immersive video service.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.286-292
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• 2023

This article is set against the backdrop of the rapid development of the metaverse and artificial intelligence technologies, and aims to explore the possibility and potential impact of integrating AI technology into the traditional 3D animation production process. Through an in-depth analysis of the differences when merging traditional production processes with AI technology, it aims to summarize a new innovative workflow for 3D animation production. This new process takes full advantage of the efficiency and intelligent features of AI technology, significantly improving the efficiency of animation production and enhancing the overall quality of the animations. Furthermore, the paper delves into the creative methods and developmental implications of artificial intelligence technology in real-time rendering engines for 3D animation. It highlights the importance of these technologies in driving innovation and optimizing workflows in the field of animation production, showcasing how they provide new perspectives and possibilities for the future development of the animation industry.