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Sequential Monte Carlo Instant Radiosity

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dc.date.accessioned 2015-09-28T12:25:59Z und
dc.date.accessioned 2017-10-24T12:24:02Z
dc.date.available 2015-09-28T12:25:59Z und
dc.date.available 2017-10-24T12:24:02Z
dc.date.issued 2015-09-28T12:25:59Z
dc.identifier.uri http://radr.hulib.helsinki.fi/handle/10138.1/5033 und
dc.identifier.uri http://hdl.handle.net/10138.1/5033
dc.title Sequential Monte Carlo Instant Radiosity en
ethesis.discipline Computer science en
ethesis.discipline Tietojenkäsittelytiede fi
ethesis.discipline Datavetenskap sv
ethesis.discipline.URI http://data.hulib.helsinki.fi/id/1dcabbeb-f422-4eec-aaff-bb11d7501348
ethesis.department.URI http://data.hulib.helsinki.fi/id/225405e8-3362-4197-a7fd-6e7b79e52d14
ethesis.department Institutionen för datavetenskap sv
ethesis.department Department of Computer Science en
ethesis.department Tietojenkäsittelytieteen laitos fi
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingfors universitet sv
ethesis.university University of Helsinki en
ethesis.university Helsingin yliopisto fi
dct.creator Hedman, Peter
dct.issued 2015
dct.language.ISO639-2 eng
dct.abstract The focus of this thesis is to accelerate the synthesis of physically accurate images using computers. Such images are generated by simulating how light flows in the scene using unbiased Monte Carlo algorithms. To date, the efficiency of these algorithms has been too low for real-time rendering of error-free images. This limits the applicability of physically accurate image synthesis in interactive contexts, such as pre-visualization or video games. We focus on the well-known Instant Radiosity algorithm by Keller [1997], that approximates the indirect light field using virtual point lights (VPLs). This approximation is unbiased and has the characteristic that the error is spread out over large areas in the image. This low-frequency noise manifests as an unwanted 'flickering' effect in image sequences if not kept temporally coherent. Currently, the limited VPL budget imposed by running the algorithm at interactive rates results in images which may noticeably differ from the ground-truth. We introduce two new algorithms that alleviate these issues. The first, clustered hierarchical importance sampling, reduces the overall error by increasing the VPL budget without incurring a significant performance cost. It uses an unbiased Monte Carlo estimator to estimate the sensor response caused by all VPLs. We reduce the variance of this estimator with an efficient hierarchical importance sampling method. The second, sequential Monte Carlo Instant Radiosity, generates the VPLs using heuristic sampling and employs non-parametric density estimation to resolve their probability densities. As a result the algorithm is able to reduce the number of VPLs that move between frames, while also placing them in regions where they bring light to the image. This increases the quality of the individual frames while keeping the noise temporally coherent — and less noticeable — between frames. When combined, the two algorithms form a rendering system that performs favourably against traditional path tracing methods, both in terms of performance and quality. Unlike prior VPL-based methods, our system does not suffer from the objectionable lack of temporal coherence in highly occluded scenes. en
dct.language en
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.urn URN:NBN:fi-fe2017112251828
dc.type.dcmitype Text

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