Dynamic polygon cloud compression

Pavéz, Eduardo; Chou, Philip A.

doi:10.1109/icassp.2017.7952694

Cited by 20 publications

(51 citation statements)

References 17 publications

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“…For the reference frame at t = 1, also known as the intra (I) frame, the I-encoder represents the vertices V (1) using voxels. The voxelized vertices V (1) v are encoded using an octree. The connectivity F (1) is compressed without a loss using an entropy coder.…”

Section: C) Compression System Overviewmentioning

confidence: 99%

“…The voxelized vertices V (1) v are encoded using an octree. The connectivity F (1) is compressed without a loss using an entropy coder. The connectivity is coded only once per GOF since it is consistent across the GOF, i.e., F (t) = F (1) for t ∈ [N].…”

Section: C) Compression System Overviewmentioning

confidence: 99%

“…The connectivity F (1) is compressed without a loss using an entropy coder. The connectivity is coded only once per GOF since it is consistent across the GOF, i.e., F (t) = F (1) for t ∈ [N]. The decoded verticesV (1) are refined and used to construct an auxiliary point cloud (V (1) r , C (1) r ).…”

Section: C) Compression System Overviewmentioning

confidence: 99%

“…We assume that the vertices in V (1) are in Morton order. If not, we put them into Morton order and permute the indices in F (1) accordingly.…”

Section: ) Geometry Encoding and Decodingmentioning

confidence: 99%

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Dynamic polygon clouds: representation and compression for VR/AR

Pavéz

Chou

Queiroz

et al. 2018

SIP

Self Cite

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We introduce the polygon cloud, a compressible representation of three-dimensional geometry (including attributes, such as color), intermediate between polygonal meshes and point clouds. Dynamic polygon clouds, like dynamic polygonal meshes and dynamic point clouds, can take advantage of temporal redundancy for compression. In this paper, we propose methods for compressing both static and dynamic polygon clouds, specifically triangle clouds. We compare triangle clouds to both triangle meshes and point clouds in terms of compression, for live captured dynamic colored geometry. We find that triangle clouds can be compressed nearly as well as triangle meshes, while being more robust to noise and other structures typically found in live captures, which violate the assumption of a smooth surface manifold, such as lines, points, and ragged boundaries. We also find that triangle clouds can be used to compress point clouds with significantly better performance than previously demonstrated point cloud compression methods. For intra-frame coding of geometry, our method improves upon octree-based intra-frame coding by a factor of 5–10 in bit rate. Inter-frame coding improves this by another factor of 2–5. Overall, our proposed method improves over the previous state-of-the-art in dynamic point cloud compression by 33% or more.

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Section: C) Compression System Overviewmentioning

confidence: 99%

Section: C) Compression System Overviewmentioning

confidence: 99%

Section: C) Compression System Overviewmentioning

confidence: 99%

“…We assume that the vertices in V (1) are in Morton order. If not, we put them into Morton order and permute the indices in F (1) accordingly.…”

Section: ) Geometry Encoding and Decodingmentioning

confidence: 99%

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Dynamic polygon clouds: representation and compression for VR/AR

Pavéz

Chou

Queiroz

et al. 2018

SIP

Self Cite

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“…A maioria dos codecs de nuvens de pontos existentes na literatura comprime a geometria primeiro e depois comprime os atributos dada a geometria. Abordagens típicas para a codificação dos atributos incluem codificação de transformada usando graph fourier transform (GFT) [3], [4], [5], [6], [7], [8], o gaussian process transform (GPT) [9], [10], e a transformada hierárquica adaptativa por região (RAHT) [11], [12]. RAHT, diferentemente da GFT e da GPT, não requer uma autodecomposição, e tem sido uma das transformadas inicialmente adotadas no MPEG PCC [1].…”

Section: Introductionunclassified

Compressão de Nuvem de Pontos Incorporando Codificação de Região de Interesse

Sandri¹,

Figueiredo²,

Chou³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Introduzimos codificação de região-de-interesse (ROI) para atributos de nuvem de pontos, utilizando uma medida de distorção de entrada ponderada, onde a ROI determina os pesos. Na codificação,é usada a transformada hierárquica adaptativa por região (RAHT), que depende de um conjunto de pesos. Usamos uma interpretação teórica da medida de distorção do RAHT para determinar quais os pesos da transformação devem ser definidos para os pesos da medida de distorção. A ROIé escolhida como a região 3D do rosto, detectada a partir de projeções 2D. Resultados experimentais mostram, subjetivamente, melhorias significativas na ROI com degradações subjetivamente insignificantes na não-ROI. Palavras-Chave-nuvem de pontos, região de interesse, RAHT.

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Discretization of Laplace-Beltrami Operator Based on Cotangent Scheme and Its Applications

Zhang

Duan

2019

Human Centered Computing

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Dynamic polygon cloud compression

Cited by 20 publications

References 17 publications

Dynamic polygon clouds: representation and compression for VR/AR

Dynamic polygon clouds: representation and compression for VR/AR

Compressão de Nuvem de Pontos Incorporando Codificação de Região de Interesse

Discretization of Laplace-Beltrami Operator Based on Cotangent Scheme and Its Applications

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