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Procedural tree generation
Tázlar, Vojtěch ; Křivánek, Jaroslav (advisor) ; Kahoun, Martin (referee)
During 3D modelling of the real world, it is often necessary to place trees into the scene. However particular model may not suit users' needs and the use of a dedicated software can be inconvenient - due to complex control, impossible integration into the used system, model quality or price. At the time of writing this work, there exist a few professional high-end applications that are not suitable for use by a potential casual user, mainly because of their price. Then there are only freely available applications which are often obsolete and suffer from various shortcomings. Therefore, it makes sense to try to develop a low-end application, targeted for casual users, where the power of the tool is not as important as simplicity and nature of control and satisfactory results. We analyze ways of modelling trees using current applications and describe known algorithms and approaches to the generation of tree models. Based on this, we implement our own tool capable of generation of diverse tree types and adaptation of their models to obstacles in the scene. 1
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Integration of the Corona renderer into the ArchiCAD software
Špaček, Jan ; Křivánek, Jaroslav (advisor) ; Vévoda, Petr (referee)
Integration of the Corona renderer into the ArchiCAD software Bachelor thesis - Jan Špaček, 2018 We present a prototype of a plug-in that integrates Corona Renderer into Archi- CAD. We strive to make Corona in ArchiCAD as easy to use as in other graphics applications, so the user interface is a major concern in our plug-in. Special focus was given to interactive rendering, which has proved to be a technical challenge in other host applications. We give a brief overview of the APIs that we use and describe the architecture and implementation of the plug-in. We also present the performance of interactive rendering on various scenes. 1
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Image Denoising Using Weighted Local Regression
Šťasta, Jakub ; Křivánek, Jaroslav (advisor) ; Elek, Oskár (referee)
The problem of accurately simulating light transport using Monte Carlo integration can be very difficult. In particular, scenes with complex illumination effects or complex materials can cause a scene to converge very slowly and demand a lot of computational time. To overcome this problem, image denoising algorithms have become popular in recent years. In this work we first review known approaches to denoising and adaptive rendering. We implement one of the promising algorithm by Moon et al. [2014] in a commercial rendering system Corona Standalone Renderer, evaluate its performance, strengths and weaknesses on 14 test scenes. These include difficult to denoise and converge rendering effects such as fine sub-pixel geometry, participating media, extreme depth of field of highlights, motion blur, and others. We propose corrections which make the algorithm more stable and robust. We show that it is possible to denoise renderings with Linear Weighted Regression only using a CPU. However, still even after our propositions, it is not possible to filter scenes in a consistent manner without over-blurring or not filtering where desired.
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Adjoint-Driven Importance Sampling in Light Transport Simulation
Vorba, Jiří ; Křivánek, Jaroslav (advisor) ; Keller, Alexander (referee) ; Wann Jensen, Henrik (referee)
Title: Adjoint-Driven Importance Sampling in Light Transport Simulation Author: RNDr. Jiří Vorba Department: Department of Software and Computer Science Education Supervisor: doc. Ing. Jaroslav Křivánek, Ph.D., Department of Software and Computer Science Education Abstract: Monte Carlo light transport simulation has recently been adopted by the movie industry as a standard tool for producing photo realistic imagery. As the industry pushes current technologies to the very edge of their possibilities, the unprecedented complexity of rendered scenes has underlined a fundamental weakness of MC light transport simulation: slow convergence in the presence of indirect illumination. The culprit of this poor behaviour is that the sam- pling schemes used in the state-of-the-art MC transport algorithms usually do not adapt to the conditions of rendered scenes. We base our work on the ob- servation that the vast amount of samples needed by these algorithms forms an abundant source of information that can be used to derive superior sampling strategies, tailored for a given scene. In the first part of this thesis, we adapt general machine learning techniques to train directional distributions for biasing scattering directions of camera paths towards incident illumination (radiance). Our approach allows progressive...
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Procedural placement of 3D objects
Hojdar, Štěpán ; Křivánek, Jaroslav (advisor) ; Kahoun, Martin (referee)
3D modelling in computer graphics often requires placing a big number of objects into the scene. This may be tedious or even impossible if done manually. A few programs exist to perform this task automatically but most of them are either too slow to place the required number of objects or too difficult to use for a non-expert user. We expand the already existing Corona Scatter program which is fast and user friendly but lacks in terms of functionality. We implement scattering regular patterns, scattering along spline objects and the ability to use spline objects to locally modify the distribution properties.
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Height map compression techniques
Lašan, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
The goal of this thesis is to design a suitable method for lossy compression of heightmap terrain data. This method should accept blocks of float samples of dimensions 2^n x 2^n as an input, for which it should be able to perform progressive decompression of mip-maps (lower-resolution representations). It should keep the reconstructed data within a certain maximum per-sample error bound for each mip-map level. This bound should be in the unit of meters and adjustable by the user. Given these constraints, it should be as efficient as possible. Our method is inspired by the second generation of progressive wavelet-based compression scheme modified to satisfy the~maximum-error constraint. We simplified this scheme by factoring out unnecessary computations in order to improve the efficiency. Our method can compress a 256x256 block in about 30 ms and decompress it in about 2 ms. Thanks to these attributes, the method can be used in a real-time planet renderer. It achieves the compression ratio of 37:1 on the whole Earth 90m/sample terrain dataset transformed and separated into square blocks, while respecting the maximum error of 5m. Powered by TCPDF (www.tcpdf.org)
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Segmentation of images with leaves of woody species
Valchová, Ivana ; Suk, Tomáš (advisor) ; Křivánek, Jaroslav (referee)
The thesis focuses on segmentation of images with leaves of woody species. The aim was to investigate existing image segmentation methods, choose suitable method for given data and implement it. The chosen method should segment existing datasets, photographs from cameras as well as photographs from lower-quality mobile phones. Inputs are scanned leaves and photographs of various quality. The thesis summarizes the general methods of image segmentation and describes own algorithm that gives us the best results. Based on the histogram, the algorithm decides whether the input is of sufficient quality and can be segmented by Otsu algorithm or is not and should be segmented using GrowCut algorithm. Next, the image is improved by morphological closing and holes filling. Finally, only the largest object is left. Results are illustrated using generated output images. Powered by TCPDF (www.tcpdf.org)
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Colour reproduction accuracy of stochastic spectral sampling: an experimental evaluation.
Hencz, Attila ; Wilkie, Alexander (advisor) ; Křivánek, Jaroslav (referee)
Title: Colour reproduction accuracy of stochastic spectral sampling: an experi- mental evaluation Author: Bc. Attila Hencz �attila.hencz@matfyz.cz� Department: Department of Software and Computer Science Education Supervisor: doc. Alexander Wilkie, Dr. �wilkie@cgg.mff.cuni.cz�, Department of Software and Computer Science Education Abstract: We investigate the colorimetric accuracy of stochastic sampling in the wavelength domain for realistic rendering purposes. In particular, we consider the impact of three specific design choices in a rendering system on the accuracy of the overall result: how exactly spectral samples are splatted into the final result spectrum, how many spectral channels one uses in the result images, and the impact of Hero Wavelength Spectral Sampling (HWSS) compared to the stand- ard monochrome technique. We provide experimental results, comparisons and evaluation of sampling high-resolution spectra that originate from measurements of real paint samples found in colour atlases, and normed light sources defined by the CIE. The latter include both smooth and spiky spectra, so the used com- bination of reflectances and light spectra provides a representative body of input values that allows to draw conclusions that are relevant for practical rendering work. Keywords: physically based image...
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GPU implementation of the irradiance and radiance caching algorithms
Bulant, Martin ; Křivánek, Jaroslav (advisor) ; Šik, Martin (referee)
The objective of this work is to create software implementing two algorithms for global ilumination computation. Iradiance and radiance caching should be implemented in CUDA framework on a graphics card (GPU). Parallel implementation on the GPU should improve algoritm speed compared to CPU implementation. The software will be written using an already done framework for global illumunation computation. That allows to focus on algorithm implementation only. This work should speed up testing of new or existing methods for global illumination computing, because saving and reusing of intermediate results can be used for other algorithms too. Powered by TCPDF (www.tcpdf.org)
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Synthesis of digital landscape surface data
Šebesta, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
A procedural generation of landscapes often meets a need for real spatial data at finer resolution that data available at the moment. We introduce a method that refines the spatial data at the coarse resolution into the finer resolution utilizing other data sources which are already at the better resolution. We construct weighted local linear statistical models from both the coarse and utility data and use the by- models-learned dependencies between the data sources to predict the needed data at better resolution. To achieve higher computational speed and evade utility data imperfection, we utilize truncated singular value decomposition which reduce a dimensionality of the data space we work with. The~method is highly modifiable and its application shows plausible real-like results. Thanks to this, the method can be of practical use for simulation software development. Powered by TCPDF (www.tcpdf.org)
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