Real-Time Depth-of-Field Rendering Using Anisotropically Filtered Mipmap Interpolation

This article presents a real-time GPU-based postfiltering method for rendering acceptable depth-of-field effects suited for virtual reality. Blurring is achieved by nonlinearly interpolating mipmap images generated from a pinhole image. Major artifacts common in the postfiltering techniques such as a bilinear magnification artifact, intensity leakage, and blurring discontinuity are practically eliminated via magnification with a circular filter, anisotropic mipmapping, and smoothing of blurring degrees. The whole framework is accelerated using GPU programs for constant and scalable real-time performance required for virtual reality. We also compare our method to recent GPU-based methods in terms of image quality and rendering performance.X11239sciescopu

Towards a filmic look and feel in real time computer graphics

Film footage has a distinct look and feel that audience can instantly recognize, making its replication desirable for computer generated graphics. This thesis presents methods capable of replicating significant portions of the film look and feel while being able to fit within the constraints imposed by real-time computer generated graphics on consumer hardware

직접 볼륨 렌더링에서 점진적 렌즈 샘플링을 사용한 피사계 심도 렌더링

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 신영길.Direct volume rendering is a widely used technique for extracting information from 3D scalar fields acquired by measurement or numerical simulation. To visualize the structure inside the volume, the voxels scalar value is often represented by a translucent color. This translucency of direct volume rendering makes it difficult to perceive the depth between the nested structures. Various volume rendering techniques to improve depth perception are mainly based on illustrative rendering techniques, and physically based rendering techniques such as depth of field effects are difficult to apply due to long computation time. With the development of immersive systems such as virtual and augmented reality and the growing interest in perceptually motivated medical visualization, it is necessary to implement depth of field in direct volume rendering. This study proposes a novel method for applying depth of field effects to volume ray casting to improve the depth perception. By performing ray casting using multiple rays per pixel, objects at a distance in focus are sharply rendered and objects at an out-of-focus distance are blurred. To achieve these effects, a thin lens camera model is used to simulate rays passing through different parts of the lens. And an effective lens sampling method is used to generate an aliasing-free image with a minimum number of lens samples that directly affect performance. The proposed method is implemented without preprocessing based on the GPU-based volume ray casting pipeline. Therefore, all acceleration techniques of volume ray casting can be applied without restrictions. We also propose multi-pass rendering using progressive lens sampling as an acceleration technique. More lens samples are progressively used for ray generation over multiple render passes. Each pixel has a different final render pass depending on the predicted maximum blurring size based on the circle of confusion. This technique makes it possible to apply a different number of lens samples for each pixel, depending on the degree of blurring of the depth of field effects over distance. This acceleration method reduces unnecessary lens sampling and increases the cache hit rate of the GPU, allowing us to generate the depth of field effects at interactive frame rates in direct volume rendering. In the experiments using various data, the proposed method generated realistic depth of field effects in real time. These results demonstrate that our method produces depth of field effects with similar quality to the offline image synthesis method and is up to 12 times faster than the existing depth of field method in direct volume rendering.직접 볼륨 렌더링(direct volume rendering, DVR)은 측정 또는 수치 시뮬레이션으로 얻은 3차원 공간의 스칼라 필드(3D scalar fields) 데이터에서 정보를 추출하는데 널리 사용되는 기술이다. 볼륨 내부의 구조를 가시화하기 위해 복셀(voxel)의 스칼라 값은 종종 반투명의 색상으로 표현된다. 이러한 직접 볼륨 렌더링의 반투명성은 중첩된 구조 간 깊이 인식을 어렵게 한다. 깊이 인식을 향상시키기 위한 다양한 볼륨 렌더링 기법들은 주로 삽화풍 렌더링(illustrative rendering)을 기반으로 하며, 피사계 심도(depth of field, DoF) 효과와 같은 물리 기반 렌더링(physically based rendering) 기법들은 계산 시간이 오래 걸리기 때문에 적용이 어렵다. 가상 및 증강 현실과 같은 몰입형 시스템의 발전과 인간의 지각에 기반한 의료영상 시각화에 대한 관심이 증가함에 따라 직접 볼륨 렌더링에서 피사계 심도를 구현할 필요가 있다. 본 논문에서는 직접 볼륨 렌더링의 깊이 인식을 향상시키기 위해 볼륨 광선투사법에 피사계 심도 효과를 적용하는 새로운 방법을 제안한다. 픽셀 당 여러 개의 광선을 사용한 광선투사법(ray casting)을 수행하여 초점이 맞는 거리에 있는 물체는 선명하게 표현되고 초점이 맞지 않는 거리에 있는 물체는 흐리게 표현된다. 이러한 효과를 얻기 위하여 렌즈의 서로 다른 부분을 통과하는 광선들을 시뮬레이션 하는 얇은 렌즈 카메라 모델(thin lens camera model)이 사용되었다. 그리고 성능에 직접적으로 영향을 끼치는 렌즈 샘플은 최적의 렌즈 샘플링 방법을 사용하여 최소한의 개수를 가지고 앨리어싱(aliasing)이 없는 이미지를 생성하였다. 제안한 방법은 기존의 GPU 기반 볼륨 광선투사법 파이프라인 내에서 전처리 없이 구현된다. 따라서 볼륨 광선투사법의 모든 가속화 기법을 제한없이 적용할 수 있다. 또한 가속 기술로 누진 렌즈 샘플링(progressive lens sampling)을 사용하는 다중 패스 렌더링(multi-pass rendering)을 제안한다. 더 많은 렌즈 샘플들이 여러 렌더 패스들을 거치면서 점진적으로 사용된다. 각 픽셀은 착란원(circle of confusion)을 기반으로 예측된 최대 흐림 정도에 따라 다른 최종 렌더링 패스를 갖는다. 이 기법은 거리에 따른 피사계 심도 효과의 흐림 정도에 따라 각 픽셀에 다른 개수의 렌즈 샘플을 적용할 수 있게 한다. 이러한 가속화 방법은 불필요한 렌즈 샘플링을 줄이고 GPU의 캐시(cache) 적중률을 높여 직접 볼륨 렌더링에서 상호작용이 가능한 프레임 속도로 피사계 심도 효과를 렌더링 할 수 있게 한다. 다양한 데이터를 사용한 실험에서 제안한 방법은 실시간으로 사실적인 피사계 심도 효과를 생성했다. 이러한 결과는 우리의 방법이 오프라인 이미지 합성 방법과 유사한 품질의 피사계 심도 효과를 생성하면서 직접 볼륨 렌더링의 기존 피사계 심도 렌더링 방법보다 최대 12배까지 빠르다는 것을 보여준다.CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Dissertation Goals 5 1.3 Main Contributions 6 1.4 Organization of Dissertation 8 CHAPTER 2 RELATED WORK 9 2.1 Depth of Field on Surface Rendering 10 2.1.1 Object-Space Approaches 11 2.1.2 Image-Space Approaches 15 2.2 Depth of Field on Volume Rendering 26 2.2.1 Blur Filtering on Slice-Based Volume Rendering 28 2.2.2 Stochastic Sampling on Volume Ray Casting 30 CHAPTER 3 DEPTH OF FIELD VOLUME RAY CASTING 33 3.1 Fundamentals 33 3.1.1 Depth of Field 34 3.1.2 Camera Models 36 3.1.3 Direct Volume Rendering 42 3.2 Geometry Setup 48 3.3 Lens Sampling Strategy 53 3.3.1 Sampling Techniques 53 3.3.2 Disk Mapping 57 3.4 CoC-Based Multi-Pass Rendering 60 3.4.1 Progressive Lens Sample Sequence 60 3.4.2 Final Render Pass Determination 62 CHAPTER 4 GPU IMPLEMENTATION 66 4.1 Overview 66 4.2 Rendering Pipeline 67 4.3 Focal Plane Transformation 74 4.4 Lens Sample Transformation 76 CHAPTER 5 EXPERIMENTAL RESULTS 78 5.1 Number of Lens Samples 79 5.2 Number of Render Passes 82 5.3 Render Pass Parameter 84 5.4 Comparison with Previous Methods 87 CHAPTER 6 CONCLUSION 97 Bibliography 101 Appendix 111Docto

Real-Time deep image rendering and order independent transparency

In computer graphics some operations can be performed in either object space or image space. Image space computation can be advantageous, especially with the high parallelism of GPUs, improving speed, accuracy and ease of implementation. For many image space techniques the information contained in regular 2D images is limiting. Recent graphics hardware features, namely atomic operations and dynamic memory location writes, now make it possible to capture and store all per-pixel fragment data from the rasterizer in a single pass in what we call a deep image. A deep image provides a state where all fragments are available and gives a more complete image based geometry representation, providing new possibilities in image based rendering techniques. This thesis investigates deep images and their growing use in real-time image space applications. A focus is new techniques for improving fundamental operation performance, including construction, storage, fast fragment sorting and sampling. A core and driving application is order-independent transparency (OIT). A number of deep image sorting improvements are presented, through which an order of magnitude performance increase is achieved, significantly advancing the ability to perform transparency rendering in real time. In the broader context of image based rendering we look at deep images as a discretized 3D geometry representation and discuss sampling techniques for raycasting and antialiasing with an implicit fragment connectivity approach. Using these ideas a more computationally complex application is investigated — image based depth of field (DoF). Deep images are used to provide partial occlusion, and in particular a form of deep image mipmapping allows a fast approximate defocus blur of up to full screen size