3DAC: Learning Attribute Compression for Point Clouds

Fang, Guangchi; Hu, Qingyong; Wang, Hanyun; Xu, Yiling; Guo, Yulan

doi:10.1109/cvpr52688.2022.01440

Cited by 20 publications

(6 citation statements)

References 43 publications

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“…Quach et al [15] folded 3D point cloud attributes onto the 2D grids and then directly applied the conventional 2D image codec for compression. Fang et al [16] designed an MLP-based entropy model to approximate the probability of RAHT coefficients. Alternatively, end-to-end PCAC was also studied.…”

Section: Related Work Point Cloud Attribute Compression (Pcac)mentioning

confidence: 99%

“…To measure the compression efficiency on real-life object point clouds, we consider point clouds widely used in standardization committees, including 4 sequences from 8i Voxelized Full Bodies (8iVFB) [20], 4 from Owlii dynamic human mesh (Owlii) [21], and 5 from Microsoft Voxelized Upper Bodies (MVUB) [22]. We select 7 of them for training and others for testing, as suggested in [16,17] ScanNet [23]. This is a large-scale indoor point cloud dataset totally containing more than 1600 scans, which is widely used in 3D scene understanding tasks.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction

Wang¹,

Ding²,

Ma³

2023

Preprint

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This work extends the multiscale structure originally developed for point cloud geometry compression to point cloud attribute compression. To losslessly encode the attribute while maintaining a low bitrate, accurate probability prediction is critical. With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency. Specifically, we first generate multiscale attribute tensors through average pooling, by which, for any two consecutive scales, the decoded lower-scale attribute can be used to estimate the attribute probability in the current scale in one shot. Additionally, in each scale, we perform the probability estimation group-wisely following a predefined grouping pattern. In this way, both cross-scale and (same-scale) cross-group correlations are exploited jointly. Furthermore, cross-color redundancy is removed by allowing inter-color processing for YCoCg/RGB alike multi-channel attributes. The proposed method not only demonstrates state-of-the-art compression efficiency with significant performance gains over the latest G-PCC on various contents but also sustains low complexity with affordable encoding and decoding runtime.

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Section: Related Work Point Cloud Attribute Compression (Pcac)mentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction

Wang¹,

Ding²,

Ma³

2023

Preprint

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“…In addition to the end-to-end approach, Fang et al [30] used an MLP-based model to replace the traditional entropy coding tool in G-PCC. They encoded RAHT coefficients using a neural model where side information including tree depth, weight, location, etc., was leveraged to estimate the probability of each coefficient for arithmetic coding.…”

Section: A Point Cloud Attributes Compressionmentioning

confidence: 99%

CARNet:Compression Artifact Reduction for Point Cloud Attribute

Ding¹,

Zhang²,

Wang³

et al. 2022

Preprint

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A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts. The proposed method first generates multiple Most-Probable Sample Offsets (MPSOs) as potential compression distortion approximations, and then linearly weights them for artifact mitigation. As such, we drive the filtered reconstruction as close to the uncompressed PCA as possible. To this end, we devise a Compression Artifact Reduction Network (CARNet) which consists of two consecutive processing phases: MPSOs derivation and MPSOs combination. The MPSOs derivation uses a two-stream network to model local neighborhood variations from direct spatial embedding and frequency-dependent embedding, where sparse convolutions are utilized to best aggregate information from sparsely and irregularly distributed points. The MPSOs combination is guided by the least square error metric to derive weighting coefficients on the fly to further capture content dynamics of input PCAs. The CARNet is implemented as an in-loop filtering tool of the G-PCC, where those linear weighting coefficients are encapsulated into the bitstream with negligible bit rate overhead. Experimental results demonstrate significant improvement over the latest G-PCC both subjectively and objectively. For example, our method offers 21.96% YUV BD-Rate (Bjøntegaard Delta Rate) reduction against the G-PCC across various commonly-used test point clouds. Compared with a recent work showing state-of-the-art performance, our work not only gains 12.95% YUV BD-Rate but also provides 30× processing speedup.

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“…Beyond RAHT, G-PCC uses prediction (of the RAHT coefficients) and joint entropy coding to obtain superior performance (Lasserre and Flynn, 2019;3DG, 2020b;Pavez et al, 2021). Recently (Fang et al, 2020) use neural methods for lossless entropy coding of the RAHT transform coefficients. Our work exceeds the RD performance of classic RAHT by 2-4 dB by introducing the flexibility of learning non-linear volumetric functions.…”

Section: Point Cloud Compressionmentioning

confidence: 99%

“…Our work exceeds the RD performance of classic RAHT by 2-4 dB by introducing the flexibility of learning non-linear volumetric functions. Our approach is orthogonal to the prediction and entropy coding in (Lasserre and Flynn, 2019;3DG, 2020b;Pavez et al, 2021;Fang et al, 2020) and all results could be improved by using combinations of these techniques.…”

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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3DAC: Learning Attribute Compression for Point Clouds

Cited by 20 publications

References 43 publications

Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction

Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction

CARNet:Compression Artifact Reduction for Point Cloud Attribute

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

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