Compression of 3D Mesh Geometry and Vertex Attributes for Mobile Graphics

Lee, Jong-Seok; Choe, Sungyul; Lee, Seungyong

doi:10.5626/jcse.2010.4.3.207

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Other authors [31] [32] also argue that P SN R is not suitable for human perception, but, as a matter of fact, other metrics would also be subjective and costly. Besides, according to Lee et al [33], humans cannot recognize difference between two images when the P SN R is larger than 30-50 dB. All experiments where performed using a computer with the following characteristics: intel 4 x Core i5 3330S CPU @ 2.7 GHz with 8.0 Giga of RAM memory under a Ubuntu 14.04 Operating System.…”

Section: Experiments and Methodologymentioning

confidence: 99%

Compressive Representation of Three-dimensional Models

Bíscaro

Lima²

2015

JIS

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Due to recent developments in data acquisition mechanisms, called 3d scanners, mesh compression has become an important tool for manipulating geometric data in several areas. In this context, a recent approach to the theory of signs called Compressive Sensing states that a signal can be recovered from far fewer samples than those provided by the classical theory. In this paper, we investigate the applicability of this new theory with the purpose of to obtain a compressive representation of geometric meshes. We developed an experiment which combines sampling, compression and reconstruction of various mesh sizes. Besides figuring compression rates, we also measured the relative error between the original mesh and the recovered mesh. We also compare two measurement techniques through their processing times, which are: the use of Gaussian matrices; and the use of Noiselet matrices. Gaussian matrices performed better in terms of processing speed, with equivalent performance in compression capacity. The results indicate that compressive sensing is very useful for mesh compression showing quite comparable results with traditional mesh compression techniques.

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Section: Experiments and Methodologymentioning

confidence: 99%

Compressive Representation of Three-dimensional Models

Bíscaro

Lima²

2015

JIS

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“…Nosúltimos anos, a compressão de dados tem sido importante para seu armazenamento e sua transmissão em diversos meios (rádio, internet, wireless, etc.). Em função do crescimento da utilização de malhas tridimensionais em aplicações como jogos e modelagens diversas, o tratamento compressivo dessas malhas ganhou relevância e se tornou objeto de estudo ((BOUZIDI et al, 2013), (LUFFEL et al, 2013), (YOON; KUIJPER, 2011), (LEE et al, 2010)).…”

Section: Apresentaçãounclassified

Representação compressiva de malhas

Lima¹

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A compressão de dadosé umaárea de muito interesse em termos computacionais devidoà necessidade de armazená-los e transmiti-los. Em particular, a compressão de malhas possui grande interesse em função do crescimento de sua utilização em jogos tridimensionais e modelagens diversas. Nosúltimos anos, uma nova teoria de aquisição e reconstrução de sinais foi desenvolvida, baseada no conceito de esparsidade na minimização da norma L 1 e na incoerência do sinal, chamada Compressive Sensing (CS). Essa teoria possui algumas características marcantes, como a aleatoriedade de amostragem e a reconstrução via minimização, de modo que a própria aquisição do sinalé feita considerando somente os coeficientes significativos. Qualquer objeto que possa ser interpretado como um sinal esparso, permite sua utilização. Assim, ao se representar esparsamente um objeto (sons, imagens)é possível aplicar a técnica de CS. Este trabalho verifica a viabilidade da aplicação da teoria de CS na compressão de malhas, de modo que seja possível um sensoreamento e representação compressivos na geometria de uma malha. Nos experimentos realizados, foram utilizadas variações dos parâmetros de entrada e técnicas de minimização da Norma L 1. Os resultados obtidos mostram que a técnica de CS pode ser utilizada como estratégia de compressão da geometria das malhas.

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Packing Vertex Data into Hardware-Decompressible Textures

Kwan

Wan

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processing unit (GPUs). This built-in hardware allows real-time, random-order decoding of data. However, vertex data must be packed into textures, and careless packing arrangements can easily disrupt data coherence. Hence, we propose an optimization approach for the best vertex data permutation that minimizes compression error. All of these result in fast and high-quality vertex data decompression for real-time rendering. To further improve the visual quality, we introduce vertex clustering to reduce the dynamic range of data during quantization. Our experiments demonstrate the effectiveness of our method for various vertex data of 3D models during rendering with the advantages of a minimized memory footprint and high frame rate.

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Compression of 3D Mesh Geometry and Vertex Attributes for Mobile Graphics

Cited by 7 publications

References 10 publications

Compressive Representation of Three-dimensional Models

Compressive Representation of Three-dimensional Models

Representação compressiva de malhas

Packing Vertex Data into Hardware-Decompressible Textures

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