On Block Prediction For Learning-Based Point Cloud Compression

Lazzarotto, Davi; Alexiou, Evangelos; Ebrahimi, Touradj

doi:10.1109/icip42928.2021.9506429

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Such an approach would constitute an end-to-end optimized alternative architecture for scalable coding. We can additionally propose the integration of our module with an intra or inter prediction algorithm, 47 used to encode the residuals between a prediction and a reference point cloud. Finally, future research could also concentrate on adapting this proposed architecture for compression of point cloud attributes.…”

Section: Discussionmentioning

confidence: 99%

Learning residual coding for point clouds

Lazzarotto

Ebrahimi

2021

Applications of Digital Image Processing XLIV

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Recent advancements in acquisition of three-dimensional models have been increasingly drawing attention to imaging modalities based on the plenoptic representations, such as light fields and point clouds. Since point cloud models can often contain millions of points, each including both geometric positions and associated attributes, efficient compression schemes are needed to enable transmission and storage of this type of media. In this paper, we present a detachable learning-based residual module for point cloud compression that allows for efficient scalable coding. Our proposed method is able to learn the encoding of residuals in any layered architecture, and is here implemented in a hybrid approach using both TriSoup and Octree modules from the G-PCC standard as its base layer. Results indicate that the proposed method can achieve performance gains in terms of ratedistortion when compared to both base layer alone, which is demonstrated both through objective metrics and subjective perception of quality in a rate-distortion framework. The source code of the proposed codec can be found at https://github.com/mmspg/learned-residual-pcc.

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Section: Discussionmentioning

confidence: 99%

Learning residual coding for point clouds

Lazzarotto

Ebrahimi

2021

Applications of Digital Image Processing XLIV

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“…Drawing inspiration from the remarkable achievements of learning-based methods in image and video compression, similar architectures have been adopted for PCGC. For static PCGC, early works employed dense 3D convolutions in autoencoder architectures for lossy PCGC [2,3,4] and block prediction [5]. Alternatively, voxel occupancy values were directly estimated [6] for lossless coding approaches.…”

Section: Related Workmentioning

confidence: 99%

Temporal Conditional Coding for Dynamic Point Cloud Geometry Compression

Huang,

Lazzarotto,

Ebrahimi

2024

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Point clouds allow for the representation of 3D multimedia content as a set of disconnected points in space. Their inherent irregular geometric nature poses a challenge to efficient compression, a critical operation for both storage and transmission. This paper proposes a VAE-inspired codec tailored for dynamic point cloud geometry compression, taking advantage of a temporal autoregressive hyperprior to enhance compression performance. Specifically, features derived from adjacent point cloud frames help build a hyperprior for conditional entropy coding. Sparse convolutions are leveraged to reach higher computational efficiency when compared to 3D dense convolutions. Remarkably, the proposed approach achieves an average 60.2% BD-rate gain against the contemporary V-PCC compression standard from MPEG.

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“…Like previous works (Zhang et al, 2014;Cohen et al, 2016;de Queiroz and Chou, 2016;Thanou et al, 2016;de Queiroz and Chou, 2017;Pavez et al, 2018;Chou et al, 2020;Krivokuća et al, 2020), both V-PCC and G-PCC compress geometry first, then compress attributes conditioned on geometry. Neural networks have been applied with some success to geometry compression (Yan et al, 2019;Quach et al, 2019;Guarda et al, 2019a,b;Guarda et al, 2020;Tang et al, 2020;Quach et al, 2020a;Milani, 2020Milani, , 2021Lazzarotto et al, 2021), but not to lossy attribute compression. Exceptions may include (Quach et al, 2020b), which uses learned neural 3D → 2D folding but compresses with conventional image coding, and Deep-PCAC (Sheng et al, 2021), which compresses attributes using a PointNet-style architecture, which is not volumetric and underperforms our framework by 2-5 dB (see Figure 12B and Supplementary Material).…”

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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On Block Prediction For Learning-Based Point Cloud Compression

Cited by 8 publications

References 14 publications

Learning residual coding for point clouds

Learning residual coding for point clouds

Temporal Conditional Coding for Dynamic Point Cloud Geometry Compression

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

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