Improving Efficiency for CUDA-based Volume Rendering by Combining Segmentation and Modified Sampling Strategies

The objective of this paper is to present a speed-up method to improve the rendering speed of ray casting at the same time obtaining high-quality images. Ray casting is the most commonly used volume rendering algorithm, and suitable for parallel processing. In order to improve the efficiency of parallel processing, the latest platform-Compute Unified Device Architecture (CUDA) is used. The speed-up method uses improved workload allocation and sampling strategies according to CUDA features. To implement this method, the optimal number of segments of each ray is dynamically selected based on the change of the corresponding visual angle, and each segment is processed by a distinct thread processor. In addition, for each segment, we apply different sampling quantity and density according to the distance weight. Rendering speed results show that our method achieves an average 70% improvement in terms of speed, and even 145% increase in some special cases, compared to conventional ray casting on Graphics Processing Unit (GPU). Speed-up ratio shows that this method can effectively improve the factors that influence efficiency of rendering. Excellent rendering performance makes this method contribute to real-time 3-D reconstruction

Ray Tracing Structured AMR Data Using ExaBricks

Structured Adaptive Mesh Refinement (Structured AMR) enables simulations to adapt the domain resolution to save computation and storage, and has become one of the dominant data representations used by scientific simulations; however, efficiently rendering such data remains a challenge. We present an efficient approach for volume- and iso-surface ray tracing of Structured AMR data on GPU-equipped workstations, using a combination of two different data structures. Together, these data structures allow a ray tracing based renderer to quickly determine which segments along the ray need to be integrated and at what frequency, while also providing quick access to all data values required for a smooth sample reconstruction kernel. Our method makes use of the RTX ray tracing hardware for surface rendering, ray marching, space skipping, and adaptive sampling; and allows for interactive changes to the transfer function and implicit iso-surfacing thresholds. We demonstrate that our method achieves high performance with little memory overhead, enabling interactive high quality rendering of complex AMR data sets on individual GPU workstations

Verifying volume rendering using discretization error analysis

pre-printWe propose an approach for verification of volume rendering correctness based on an analysis of the volume rendering integral, the basis of most DVR algorithms. With respect to the most common discretization of this continuous model (Riemann summation), we make assumptions about the impact of parameter changes on the rendered results and derive convergence curves describing the expected behavior. Specifically, we progressively refine the number of samples along the ray, the grid size, and the pixel size, and evaluate how the errors observed during refinement compare against the expected approximation errors. We derive the theoretical foundations of our verification approach, explain how to realize it in practice, and discuss its limitations. We also report the errors identified by our approach when applied to two publicly available volume rendering packages

AMM: Adaptive Multilinear Meshes

We present Adaptive Multilinear Meshes (AMM), a new framework that significantly reduces the memory footprint compared to existing data structures. AMM uses a hierarchy of cuboidal cells to create continuous, piecewise multilinear representation of uniformly sampled data. Furthermore, AMM can selectively relax or enforce constraints on conformity, continuity, and coverage, creating a highly adaptive and flexible representation to support a wide range of use cases. AMM supports incremental updates in both spatial resolution and numerical precision establishing the first practical data structure that can seamlessly explore the tradeoff between resolution and precision. We use tensor products of linear B-spline wavelets to create an adaptive representation and illustrate the advantages of our framework. AMM provides a simple interface for evaluating the function defined on the adaptive mesh, efficiently traversing the mesh, and manipulating the mesh, including incremental, partial updates. Our framework is easy to adopt for standard visualization and analysis tasks. As an example, we provide a VTK interface, through efficient on-demand conversion, which can be used directly by corresponding tools, such as VisIt, disseminating the advantages of faster processing and a smaller memory footprint to a wider audience. We demonstrate the advantages of our approach for simplifying scalar-valued data for commonly used visualization and analysis tasks using incremental construction, according to mixed resolution and precision data streams

Feature-preserving Reduction and Visualization of Industrial CT data using GLCM texture analysis and Mass-spring Model Deformation

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 8. 신영길.본 논문에서는 3D 볼륨 데이터에서 중요한 영역을 보존하면서 크기를 줄이는 방법을 제안한다. 볼륨 데이터에서 어느 부분이 중요한 영역인지를 결정하기 위해 질감 분석 방법 중 하나인 GLCM 균일도를 이용한 중요도 측정 모델을 제안하고, 이를 기반으로 한 MSM 기반의 볼륨 변형을 수행한다. 중요도가 반영된 볼륨 변형 과정을 통해, 중요한 영역은 상대적으로 크기가 확장되는 반면, 덜 중요한 영역은 줄어들게 된다. 이로 인해, 일반적으로 손실률이 높은 균일 다운샘플링을 이용한 압축 후에도 작은 크기의 중요한 특징점들이 손실되지 않고 보존될 수 있다. 실측 산업 영상 데이터를 이용한 실험을 통해, 그냥 균일 다운샘플링을 이용한 압축 결과에서는 사라진 작은 기공이나 수축 균열 형태의 결함 영역이 제안 방법에서는 보존되는 것을 확인할 수 있었다. 이 변형 볼륨을 원래 형태로 가시화하기 위해선 역변형 과정을 추가로 수행해야 하지만, 이 계산은 가시화 과정에 간단하게 추가할 수 있으며, 결과를 얻기 위한 소요시간에 유의미한 영향을 미치지 않는다.Non-destructive testing is a method which examines the internal structures of industrial components such as various machine parts without dissecting them. Recently, 3D CT based analysis enables more accurate inspection than traditional X-ray based tests. However, manipulating volumetric data acquired by CT is still challenging due to its huge size of the volume data. This dissertation proposes a novel method that reduces the size of 3D volume data while preserving important features in the data. Our method quantifies the importance of features in the 3D data based on gray level co-occurrence matrix (GLCM) texture analysis and represents the volume data using a simple mass-spring model. According to the measured importance value, blocks containing important features expand while other blocks shrink. After deformation, small features are exaggerated on deformed volume space, and more likely to survive during the uniform volume reduction. Experimental results showed that our method well preserved the small features of the original volume data during the reduction without any artifact comparing with the previous methods. Although additional inverse deformation process was required for the rendering of the deformed volume data, the rendering speed of the deformed volume data was much faster than that of the original volume data.초록 i 목차 iii 그림 목차 vi 표 목차 x 1장 서론 1 1.1 볼륨 렌더링 1 1.2 비파괴검사 2 1.3 연구 내용 4 1.4 논문의 구성 6 2장 관련 연구 7 2.1 볼륨 렌더링 알고리즘 7 2.1.1 볼륨 데이터의 특성 7 2.1.2 표면 추출 기법 8 2.1.3 직접 볼륨 렌더링 10 2.2 압축 볼륨 렌더링 17 2.2.1 벡터 양자화 18 2.2.2 변환 부호화 19 2.2.3 다중-해상도 기반 기법 23 2.2.4 볼륨 변형 기반 방법 25 2.3 질량-스프링 기반 볼륨 변형 모델 27 2.4 산업용 CT 영상의 중요 특징점 측량 방법 30 3장 중요도 측정 기법 32 3.1 명암도 동시발생 행렬 32 3.2 GLCM 균일도 기반 중요도 모델 36 3.3 공기 영역 제거 44 4장 볼륨 변형, 축소 및 가시화 47 4.1 질량-스프링 모델 기반 볼륨 변형 47 4.2 볼륨 축소 54 4.3 역변형 및 렌더링 55 5장 실험 및 결과 58 5.1 화질 평가 60 5.2 속도 평가 65 5.3 파라미터 연구 69 6장 결론 74 6.1 요약 74 6.2 향후 연구 75 참고문헌 77 Abstract 83Docto

Doctor of Philosophy

dissertationIn this dissertation, we advance the theory and practice of verifying visualization algorithms. We present techniques to assess visualization correctness through testing of important mathematical properties. Where applicable, these techniques allow us to distinguish whether anomalies in visualization features can be attributed to the underlying physical process or to artifacts from the implementation under verification. Such scientific scrutiny is at the heart of verifiable visualization - subjecting visualization algorithms to the same verification process that is used in other components of the scientific pipeline. The contributions of this dissertation are manifold. We derive the mathematical framework for the expected behavior of several visualization algorithms, and compare them to experimentally observed results in the selected codes. In the Computational Science & Engineering community CS&E, this technique is know as the Method of Manufactured Solution (MMS). We apply MMS to the verification of geometrical and topological properties of isosurface extraction algorithms, and direct volume rendering. We derive the convergence of geometrical properties of isosurface extraction techniques, such as function value and normals. For the verification of topological properties, we use stratified Morse theory and digital topology to design algorithms that verify topological invariants. In the case of volume rendering algorithms, we provide the expected discretization errors for three different error sources. The results of applying the MMS is another important contribution of this dissertation. We report unexpected behavior for almost all implementations tested. In some cases, we were able to find and fix bugs that prevented the correctness of the visualization algorithm. In particular, we address an almost 2 0 -year-old bug with the core disambiguation procedure of Marching Cubes 33, one of the first algorithms intended to preserve the topology of the trilinear interpolant. Finally, an important by-product of this work is a range of responses practitioners can expect to encounter with the visualization technique under verification