Point Cloud Compression Incorporating Region of Interest Coding

Sandri, Gustavo; Figueiredo, Victor F.; Chou, Philip A.; Queiroz, Ricardo L. de

doi:10.1109/icip.2019.8803553

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…The attribute compression in G-PCC uses linear transforms, which adapt based on the geometry. A core transform is the region-adaptive hierarchical transform (RAHT) (de Queiroz and Sandri G. P. et al, 2019), which is a linear transform that is orthonormal with respect to a discrete measure whose mass is put on the point cloud geometry (Sandri et al, 2019a;Chou et al, 2020). Thus RAHT compresses attributes conditioned on geometry.…”

Section: Point Cloud Compressionmentioning

confidence: 99%

LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks

Berivan¹,

Chou²,

Hwang³

et al. 2022

Front. Signal Process.

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We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We model the volumetric function by tiling space into blocks, and representing the function over each block by shifts of a coordinate-based, or implicit, neural network. Inputs to the network include both spatial coordinates and a latent vector per block. We represent the latent vectors using coefficients of the region-adaptive hierarchical transform (RAHT) used in the MPEG geometry-based point cloud codec G-PCC. The coefficients, which are highly compressible, are rate-distortion optimized by back-propagation through a rate-distortion Lagrangian loss in an auto-decoder configuration. The result outperforms the transform in the current standard, RAHT, by 2–4 dB and a recent non-volumetric method, Deep-PCAC, by 2–5 dB at the same bit rate. This is the first work to compress volumetric functions represented by local coordinate-based neural networks. As such, we expect it to be applicable beyond point clouds, for example to compression of high-resolution neural radiance fields.

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Section: Point Cloud Compressionmentioning

confidence: 99%

LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks

Berivan¹,

Chou²,

Hwang³

et al. 2022

Front. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The attribute compression in G-PCC uses linear transforms, which adapt based on the geometry. A core transform is the region-adaptive hierarchical transform (RAHT) [20,68], which is a linear transform that is orthonormal with respect to a discrete measure whose mass is put on the point cloud geometry [16,67]. Thus RAHT compresses attributes conditioned on geometry.…”

Section: Point Cloud Compressionmentioning

confidence: 99%

LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks

Berivan¹,

Chou²,

Hwang³

et al. 2021

Preprint

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We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the volumetric function. We model the volumetric function by tiling space into blocks, and representing the function over each block by shifts of a coordinatebased, or implicit, neural network. Inputs to the network include both spatial coordinates and a latent vector per block. We represent the latent vectors using coefficients of the region-adaptive hierarchical transform (RAHT) used in the MPEG geometry-based point cloud codec G-PCC. The coefficients, which are highly compressible, are ratedistortion optimized by back-propagation through a ratedistortion Lagrangian loss in an auto-decoder configuration. The result outperforms RAHT by 2-4 dB. This is the first work to compress volumetric functions represented by local coordinate-based neural networks. As such, we expect it to be applicable beyond point clouds, for example to compression of high-resolution neural radiance fields.

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“…In a point cloud, the vertices V = [vi] represent the coordinates (x, y, z) of real points in space; the attributes a = [ai] represent colors or other attributes of the points; and the weights q = [qi] represent the relative importance of the points. The weights are usually set to be constant (qi = 1), but may be adjusted to reflect different regions of interest [17]. We assume points are voxelized.…”

Section: Application To Point Cloudsmentioning

confidence: 99%

Region Adaptive Graph Fourier Transform for 3D Point Clouds

Pavéz

Girault

Ortega

et al. 2020

2020 IEEE International Conference on Image Processing (ICIP)

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We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for compression of 3D point cloud attributes. We assume the points are organized by a family of nested partitions represented by a tree. The RA-GFT is a multiresolution transform, formed by combining spatially localized block transforms. At each resolution level, attributes are processed in clusters by a set of block transforms. Each block transform produces a single approximation (DC) coefficient, and various detail (AC) coefficients. The DC coefficients are promoted up the tree to the next (lower resolution) level, where the process can be repeated until reaching the root. Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient. For this, we introduce the Q-normalized graph Laplacian, and propose using its eigenvectors as the block transform. The RA-GFT outperforms the Region Adaptive Haar Transform (RAHT) by up to 2.5 dB, with a small complexity overhead.

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Point Cloud Compression Incorporating Region of Interest Coding

Cited by 10 publications

References 13 publications

LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks

LVAC: Learned volumetric attribute compression for point clouds using coordinate based networks

LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks

Region Adaptive Graph Fourier Transform for 3D Point Clouds

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