Physically Based Rendering Second Edition Pdf Download UPD
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This new edition greatly refines its best-selling predecessor byadding sections on bidirectional light transport; stochasticprogressive photon mapping; a significantly-improved subsurfacescattering implementation; numerical robustness issues in ray-objectintersection; microfacet reflection models; realistic camera models;and much more. These updates reflect the current state-of-the-arttechnology, and along with the lucid pairing of text and code, ensurethe book's leading position as a reference text for those working inrendering.
For a preview, you can downloadChapter 4, Primitives andIntersection Acceleration from the second edition,or Chapter 11, VolumeScatteringand Chapter 14, LightTransport II: Volume Rendering from the fourth edition.
Greg Humphreys is Director of Engineering at FanDuel, havingpreviously worked on the Chrome graphics team at Google and theOptiX GPU raytracing engine at NVIDIA. Before that, he was aprofessor of Computer Science at the University of Virginia,where he conducted research in both high performance andphysically based computer graphics, as well as computerarchitecture and visualization. Greg has a B.S.E. degree fromPrinceton, and a Ph.D. in Computer Science from Stanford underthe supervision of Pat Hanrahan. When he's not tracing rays,Greg can usually be found playing tournament bridge.
This new edition greatly refines its best-selling predecessor by streamlining all obsolete code as well as adding sections on parallel rendering and system design; animating transformations; multispectral rendering; realistic lens systems; blue noise and adaptive sampling patterns and reconstruction; measured BRDFs; and instant global illumination, as well as subsurface and multiple-scattering integrators.
Through the ideas and software in this book, users will learn to design and employ a fully-featured rendering system for creating stunning imagery. This completely updated and revised edition includes new coverage on ray-tracing hair and curves primitives, numerical precision issues with ray tracing, LBVHs, realistic camera models, the measurement equation, and much more. It is a must-have, full color resource on physically-based rendering.
AbstractTo improve the target detection accuracy and speed of autonomous driving in various weather environments and small target traffic senarios,an improved YOLOV4 target detection model based on CSPDarknet45_G backbone network is proposed in this paper.By adding a new DBG module which consists of DArknetConv2D + BN + GELU activation function,this model is enhanced in generalization ability and accuracy. We also improved Res unit residual module to enhance shallow features fusing with deep feathers and reduced the number of neurons in the CSP module to simplify the module structure.The K-Means++ clustering algorithm is introduced to obtain the size of the prior box used for target detection to satisfy the data set in this paper. In the captured target vehicle image data set, the model detection result shows that the improved YOLOV4 model achieve an average detection accuracy of 90.45%, a recall of 94.37%, and an FPS of 50 frames per second when the IOU is taken as 0.5, which meet the real-time and accuracy of the detection task in this paper.
To attain a more reliable and accurate diagnosis, recently, varieties of computer-aided detection (CAD) and diagnosis (CADx) approaches have been developed to assist interpretation of the medical images. At least four types of efforts may be identified among these CAD and CADx approaches. The first type is to assist in visual detection and qualitative analysis of the objects of interest in the medical images by either enhancing the salient features of the objects or suppressing the background noises. The second type is to assist in extraction of the objects of interest for further quantitative analyses by such techniques as boundary delineation, tree-structure reconstruction, and fiber tracking. The third type is to automatically detect and classify the objects of interest by integrating the data mining, medical image analysis, and signal processing technologies. The fourth type is to estimate the anatomical and functional tissue properties not explicitly revealed in the medical images based on mathematical modeling, for example, physiology, biomechanics, heat transfer, and so forth. 153554b96e
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