2,623 research outputs found

    Efficient volumetric mapping of multi-scale environments using wavelet-based compression

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    Volumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation. Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of precise measurements. Handling this data in a computationally and memory-efficient manner is paramount to representing the environment at the desired scales and resolutions. In this work, we express the desirable properties of a volumetric mapping framework through the lens of multi-resolution analysis. This shows that wavelets are a natural foundation for hierarchical and multi-resolution volumetric mapping. Based on this insight we design an efficient mapping system that uses wavelet decomposition. The efficiency of the system enables the use of uncertainty-aware sensor models, improving the quality of the maps. Experiments on both synthetic and real-world data provide mapping accuracy and runtime performance comparisons with state-of-the-art methods on both RGB-D and 3D LiDAR data. The framework is open-sourced to allow the robotics community at large to explore this approach.Comment: 11 pages, 6 figures, 2 tables, accepted to RSS 2023, code is open-source: https://github.com/ethz-asl/wavema

    Efficient From-Point Visibility for Global Illumination in Virtual Scenes with Participating Media

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    Sichtbarkeitsbestimmung ist einer der fundamentalen Bausteine fotorealistischer Bildsynthese. Da die Berechnung der Sichtbarkeit allerdings รคuรŸerst kostspielig zu berechnen ist, wird nahezu die gesamte Berechnungszeit darauf verwendet. In dieser Arbeit stellen wir neue Methoden zur Speicherung, Berechnung und Approximation von Sichtbarkeit in Szenen mit streuenden Medien vor, die die Berechnung erheblich beschleunigen, dabei trotzdem qualitativ hochwertige und artefaktfreie Ergebnisse liefern

    The Iray Light Transport Simulation and Rendering System

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    While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

    Doctor of Philosophy

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    dissertationWhile boundary representations, such as nonuniform rational B-spline (NURBS) surfaces, have traditionally well served the needs of the modeling community, they have not seen widespread adoption among the wider engineering discipline. There is a common perception that NURBS are slow to evaluate and complex to implement. Whereas computer-aided design commonly deals with surfaces, the engineering community must deal with materials that have thickness. Traditional visualization techniques have avoided NURBS, and there has been little cross-talk between the rich spline approximation community and the larger engineering field. Recently there has been a strong desire to marry the modeling and analysis phases of the iterative design cycle, be it in car design, turbulent flow simulation around an airfoil, or lighting design. Research has demonstrated that employing a single representation throughout the cycle has key advantages. Furthermore, novel manufacturing techniques employing heterogeneous materials require the introduction of volumetric modeling representations. There is little question that fields such as scientific visualization and mechanical engineering could benefit from the powerful approximation properties of splines. In this dissertation, we remove several hurdles to the application of NURBS to problems in engineering and demonstrate how their unique properties can be leveraged to solve problems of interest

    New Geometric Data Structures for Collision Detection

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    We present new geometric data structures for collision detection and more, including: Inner Sphere Trees - the first data structure to compute the peneration volume efficiently. Protosphere - an new algorithm to compute space filling sphere packings for arbitrary objects. Kinetic AABBs - a bounding volume hierarchy that is optimal in the number of updates when the objects deform. Kinetic Separation-List - an algorithm that is able to perform continuous collision detection for complex deformable objects in real-time. Moreover, we present applications of these new approaches to hand animation, real-time collision avoidance in dynamic environments for robots and haptic rendering, including a user study that exploits the influence of the degrees of freedom in complex haptic interactions. Last but not least, we present a new benchmarking suite for both, peformance and quality benchmarks, and a theoretic analysis of the running-time of bounding volume-based collision detection algorithms

    From 3D Models to 3D Prints: an Overview of the Processing Pipeline

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    Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

    ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ์ ์ง„์  ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง์„ ์‚ฌ์šฉํ•œ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ๋ Œ๋”๋ง

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ์‹ ์˜๊ธธ.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.์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง(direct volume rendering, DVR)์€ ์ธก์ • ๋˜๋Š” ์ˆ˜์น˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜์œผ๋กœ ์–ป์€ 3์ฐจ์› ๊ณต๊ฐ„์˜ ์Šค์นผ๋ผ ํ•„๋“œ(3D scalar fields) ๋ฐ์ดํ„ฐ์—์„œ ์ •๋ณด๋ฅผ ์ถ”์ถœํ•˜๋Š”๋ฐ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ์ˆ ์ด๋‹ค. ๋ณผ๋ฅจ ๋‚ด๋ถ€์˜ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์‹œํ™”ํ•˜๊ธฐ ์œ„ํ•ด ๋ณต์…€(voxel)์˜ ์Šค์นผ๋ผ ๊ฐ’์€ ์ข…์ข… ๋ฐ˜ํˆฌ๋ช…์˜ ์ƒ‰์ƒ์œผ๋กœ ํ‘œํ˜„๋œ๋‹ค. ์ด๋Ÿฌํ•œ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๋ฐ˜ํˆฌ๋ช…์„ฑ์€ ์ค‘์ฒฉ๋œ ๊ตฌ์กฐ ๊ฐ„ ๊นŠ์ด ์ธ์‹์„ ์–ด๋ ต๊ฒŒ ํ•œ๋‹ค. ๊นŠ์ด ์ธ์‹์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋ณผ๋ฅจ ๋ Œ๋”๋ง ๊ธฐ๋ฒ•๋“ค์€ ์ฃผ๋กœ ์‚ฝํ™”ํ’ ๋ Œ๋”๋ง(illustrative rendering)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋ฉฐ, ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„(depth of field, DoF) ํšจ๊ณผ์™€ ๊ฐ™์€ ๋ฌผ๋ฆฌ ๊ธฐ๋ฐ˜ ๋ Œ๋”๋ง(physically based rendering) ๊ธฐ๋ฒ•๋“ค์€ ๊ณ„์‚ฐ ์‹œ๊ฐ„์ด ์˜ค๋ž˜ ๊ฑธ๋ฆฌ๊ธฐ ๋•Œ๋ฌธ์— ์ ์šฉ์ด ์–ด๋ ต๋‹ค. ๊ฐ€์ƒ ๋ฐ ์ฆ๊ฐ• ํ˜„์‹ค๊ณผ ๊ฐ™์€ ๋ชฐ์ž…ํ˜• ์‹œ์Šคํ…œ์˜ ๋ฐœ์ „๊ณผ ์ธ๊ฐ„์˜ ์ง€๊ฐ์— ๊ธฐ๋ฐ˜ํ•œ ์˜๋ฃŒ์˜์ƒ ์‹œ๊ฐํ™”์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„๋ฅผ ๊ตฌํ˜„ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๊นŠ์ด ์ธ์‹์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•์— ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ ์šฉํ•˜๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ํ”ฝ์…€ ๋‹น ์—ฌ๋Ÿฌ ๊ฐœ์˜ ๊ด‘์„ ์„ ์‚ฌ์šฉํ•œ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•(ray casting)์„ ์ˆ˜ํ–‰ํ•˜์—ฌ ์ดˆ์ ์ด ๋งž๋Š” ๊ฑฐ๋ฆฌ์— ์žˆ๋Š” ๋ฌผ์ฒด๋Š” ์„ ๋ช…ํ•˜๊ฒŒ ํ‘œํ˜„๋˜๊ณ  ์ดˆ์ ์ด ๋งž์ง€ ์•Š๋Š” ๊ฑฐ๋ฆฌ์— ์žˆ๋Š” ๋ฌผ์ฒด๋Š” ํ๋ฆฌ๊ฒŒ ํ‘œํ˜„๋œ๋‹ค. ์ด๋Ÿฌํ•œ ํšจ๊ณผ๋ฅผ ์–ป๊ธฐ ์œ„ํ•˜์—ฌ ๋ Œ์ฆˆ์˜ ์„œ๋กœ ๋‹ค๋ฅธ ๋ถ€๋ถ„์„ ํ†ต๊ณผํ•˜๋Š” ๊ด‘์„ ๋“ค์„ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ํ•˜๋Š” ์–‡์€ ๋ Œ์ฆˆ ์นด๋ฉ”๋ผ ๋ชจ๋ธ(thin lens camera model)์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์„ฑ๋Šฅ์— ์ง์ ‘์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ผ์น˜๋Š” ๋ Œ์ฆˆ ์ƒ˜ํ”Œ์€ ์ตœ์ ์˜ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์ตœ์†Œํ•œ์˜ ๊ฐœ์ˆ˜๋ฅผ ๊ฐ€์ง€๊ณ  ์•จ๋ฆฌ์–ด์‹ฑ(aliasing)์ด ์—†๋Š” ์ด๋ฏธ์ง€๋ฅผ ์ƒ์„ฑํ•˜์˜€๋‹ค. ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ๊ธฐ์กด์˜ GPU ๊ธฐ๋ฐ˜ ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ• ํŒŒ์ดํ”„๋ผ์ธ ๋‚ด์—์„œ ์ „์ฒ˜๋ฆฌ ์—†์ด ๊ตฌํ˜„๋œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณผ๋ฅจ ๊ด‘์„ ํˆฌ์‚ฌ๋ฒ•์˜ ๋ชจ๋“  ๊ฐ€์†ํ™” ๊ธฐ๋ฒ•์„ ์ œํ•œ์—†์ด ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๊ฐ€์† ๊ธฐ์ˆ ๋กœ ๋ˆ„์ง„ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง(progressive lens sampling)์„ ์‚ฌ์šฉํ•˜๋Š” ๋‹ค์ค‘ ํŒจ์Šค ๋ Œ๋”๋ง(multi-pass rendering)์„ ์ œ์•ˆํ•œ๋‹ค. ๋” ๋งŽ์€ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋“ค์ด ์—ฌ๋Ÿฌ ๋ Œ๋” ํŒจ์Šค๋“ค์„ ๊ฑฐ์น˜๋ฉด์„œ ์ ์ง„์ ์œผ๋กœ ์‚ฌ์šฉ๋œ๋‹ค. ๊ฐ ํ”ฝ์…€์€ ์ฐฉ๋ž€์›(circle of confusion)์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์˜ˆ์ธก๋œ ์ตœ๋Œ€ ํ๋ฆผ ์ •๋„์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ์ตœ์ข… ๋ Œ๋”๋ง ํŒจ์Šค๋ฅผ ๊ฐ–๋Š”๋‹ค. ์ด ๊ธฐ๋ฒ•์€ ๊ฑฐ๋ฆฌ์— ๋”ฐ๋ฅธ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ์˜ ํ๋ฆผ ์ •๋„์— ๋”ฐ๋ผ ๊ฐ ํ”ฝ์…€์— ๋‹ค๋ฅธ ๊ฐœ์ˆ˜์˜ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ์„ ์ ์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฐ€์†ํ™” ๋ฐฉ๋ฒ•์€ ๋ถˆํ•„์š”ํ•œ ๋ Œ์ฆˆ ์ƒ˜ํ”Œ๋ง์„ ์ค„์ด๊ณ  GPU์˜ ์บ์‹œ(cache) ์ ์ค‘๋ฅ ์„ ๋†’์—ฌ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์—์„œ ์ƒํ˜ธ์ž‘์šฉ์ด ๊ฐ€๋Šฅํ•œ ํ”„๋ ˆ์ž„ ์†๋„๋กœ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ๋ Œ๋”๋ง ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•œ๋‹ค. ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ ์‹คํ—˜์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์€ ์‹ค์‹œ๊ฐ„์œผ๋กœ ์‚ฌ์‹ค์ ์ธ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ƒ์„ฑํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์šฐ๋ฆฌ์˜ ๋ฐฉ๋ฒ•์ด ์˜คํ”„๋ผ์ธ ์ด๋ฏธ์ง€ ํ•ฉ์„ฑ ๋ฐฉ๋ฒ•๊ณผ ์œ ์‚ฌํ•œ ํ’ˆ์งˆ์˜ ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ํšจ๊ณผ๋ฅผ ์ƒ์„ฑํ•˜๋ฉด์„œ ์ง์ ‘ ๋ณผ๋ฅจ ๋ Œ๋”๋ง์˜ ๊ธฐ์กด ํ”ผ์‚ฌ๊ณ„ ์‹ฌ๋„ ๋ Œ๋”๋ง ๋ฐฉ๋ฒ•๋ณด๋‹ค ์ตœ๋Œ€ 12๋ฐฐ๊นŒ์ง€ ๋น ๋ฅด๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค.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
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