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. Therefore, we drive the filtered reconstruction as closely to the uncompressed PCA as possible. To this end, we devise an attribute artifact reduction network (ARNet) consisting 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-squares error metric to derive weighting coefficients on the fly to further capture the content dynamics of the input PCAs. ARNet is implemented as an in-loop filtering tool for G-PCC, where the linear weighting coefficients are encapsulated into the bitstream with negligible bitrate overhead. The experimental results demonstrate significant improvements over the latest G-PCC both subjectively and objectively. For example, our method offers a 22.12% YUV Bj⊘ntegaard delta rate (BD-Rate) reduction compared to G-PCC across various commonly used test point clouds. Compared with a recent study showing state-of-the-art performance, our work not only gains 13.23% YUV BD-Rate but also provides a 30 × processing speedup.

Point cloud video streaming is promising for immersive media applications, which urges the development of efficient compression methods. However, existing approaches either suffer from poor performance or lack effective coder control mechanisms, making them impractical for networked point cloud services, where bandwidth is often constrained and fluctuates over time. Therefore, this paper proposes a system-level solution - a layered point cloud compressor, called Yak, to address these issues. Yak offers comprehensive support for both intra and inter-frame coding of geometry and attribute components in point cloud sequences. It consists of three layers: the Base Layer uses the standard G-PCC to encode a thumbnail counterpart downscaled from the input point cloud; the Enhancement Layer devises the end-to-end variational autoencoder to compress the original input conditioned on the base layer reconstruction, and the Dynamic Layer generates feature-space predictions as the temporal prior for conditional inter-frame coding. In addition, Yak devises the Content Analysis module to dynamically determine the optimal encoding parameters of each frame, by which bit budget is intelligently allocated for geometry and attribute components to maximize the overall rate-distortion (R-D) performance. Such accurate rate control relies on the parametric rate/distortion models whose parameters are initialized through one-pass template matching and frame-wise delta updating constrained by R-D optimization. Following standard evaluation guidelines, Yak has notably outperformed traditional rules-based methods such as MPEG G-PCC and V-PCC, as well as other learning-based approaches, while offering flexible networked adaption and affordable complexity.

This paper develops a Scalable Point Cloud Attribute Compression solution, termedScalablePCAC. In a two-layer example,ScalablePCACuses the standard G-PCC at the base layer to directly encode the thumbnail point cloud that is downscaled from the original input, and a learning-based model at the enhancement layer to compress and restore the full-resolution input point cloud conditioned on the base layer reconstruction. As such, the base layer provides a coarse reconstruction of the input point cloud and the enhancement layer further improves the quality. We then adopt a cross-layer rate allocation strategy that flexibly determines the resolution downscaling factor, the quantization parameter of the base layer, and the quality controlling factor of the enhancement layer to adapt the bitrate of the two layers for approximately optimal Rate-Distortion (R-D) performance. We conduct extensive experiments on popular point clouds following the MPEG common test conditions. Results demonstrate that the proposedScalablePCACachieves$>$10% BD-BR reduction against the latest G-PCC version 22 (TMC13v22) on the Y component; it also significantly outperforms existing learning-based solutions for point cloud attribute compression,e.g., compared with a recent work showing state-of-the-art performance, it achieves$>$20% BD-BR reduction.

The emergence of holographic media drives the standardization of Geometry-based Point Cloud Compression (G-PCC) to sustain networked service provisioning. However, G-PCC inevitably introduces visually annoying artifacts, degrading the quality of experience (QoE). This work focuses on restoring G-PCC compressed point cloud attributes, e.g., RGB colors, to which fully data-driven and rules-unrolling-based post-processing filters are studied. At first, as compressed attributes exhibit nested blockiness, we develop a learning-based sample adaptive offset (NeuralSAO), which leverages a neural model using multiscale feature aggregation and embedding to characterize local correlations for quantization error compensation. Later, given statistically Gaussian distributed quantization noise, we suggest the utilization of a bilateral filter with Gaussian kernels to weigh neighbors by jointly considering their geometric and photometric contributions for restoration. Since local signals often present varying distributions, we propose estimating the smoothing parameters of the bilateral filter using an ultra-lightweight neural model. Such a bilateral filter with learnable parameters is called NeuralBF. The proposed NeuralSAO demonstrates the state-of-art restoration quality improvement, e.g., 20% BD-BR (Bjøntegaard delta rate) reduction over G-PCC on solid points clouds. However, NeuralSAO is computationally intensive and may suffer from poor generalization. On the other hand, although NeuralBF only achieves half of the gains of NeuralSAO, it is lightweight and exhibits impressive generalization across various samples. This comparative study between the data-driven large-scale NeuralSAO and the rules-unrolling-based small-scale NeuralBF helps to understand the capacity (i.e., performance, complexity, generalization) of underlying filters in terms of the quality restoration for compressed point cloud attribute.

As a promising technique for 3D representation, 3D Gaussian Splatting (3DGS) offers fast rendering speed and high fidelity while generating large data volumes. This challenges storage and transmission, so an efficient compression solution is required. Existing implicit methods require pre-scene optimization (online), leading to a long optimization time. By contrast, this paper regards the 3DGS as a point cloud and pre-trains a generalizable (offline) neural coder for compression. After obtaining the 3DGS representation, we focus on the data compression process, which is friendly to applications already equipped with a PCC codec. The neural coder employed is extended from a typical AIbased point cloud compression method, which uses a multiscale and multistage framework to exploit spatial correlations across scales and stages for conditional coding. Experimental results show that our method significantly outperforms existing 3DGS representations without compromising fidelity, achieving more than 39× and 6.8× compression ratio compared to the original 3DGS and SOTA Scaffold-GS, respectively. More importantly, our approach does not require additional time to optimize the compression model.

The Geometry-based Point Cloud Compression (G-PCC) standard enables point cloud delivery over the internet through efficient compression. Limited by the transmission bandwidth, rate control is demanded in G-PCC for high-quality point cloud video streaming. This paper thus proposes a content-aware rate control solution for G-PCC. Given the target bitrate and distortion evaluation criteria, our method can predict the geometry and attribute quantizers for G-PCC while minimizing the overall distortion. Specifically, as the rate and distortion of both geometry and attribute are involved in G-PCC, we separately establish rate/distortion models for geometry and attribute. Moreover, recognizing the dependence of attribute compression on reconstructed geometry, we integrate the geometry quantizer into the attribute rate/distortion models to improve prediction accuracy. For dynamic coding scenarios, we leverage selective representative frames for efficient model parameter initialization. Additionally, we introduce a μ updating strategy that dynamically incorporates information from previous frames to update the existing models. Extensive experiments demonstrate the effectiveness of our proposed method. Under the G-PCC common test condition, our method achieves remarkable rate accuracy, with a 5.3% bitrate error for static coding and 0.3% for dynamic coding. Moreover, it achieves >15% BD-Rate gains over the G-PCC anchor. These results showcase its capabilities in delivering high-fidelity point cloud video streams within the bandwidth constraint.

A learning-based YOGA (Yet Another Geometry-based Point Cloud Compressor) is proposed. It is flexible, allowing for the separable lossy compression of geometry and color attributes, and variable-rate coding using a single neural model; it is high-efficiency, significantly outperforming the latest G-PCC standard quantitatively and qualitatively, e.g., 25% BD-BR gains using PCQM (Point Cloud Quality Metric) as the distortion assessment, and it is lightweight, e.g., similar runtime as the G-PCC codec, owing to the use of sparse convolution and parallel entropy coding. To this end, YOGA adopts a unified end-to-end learning-based backbone for separate geometry and attribute compression. The backbone uses a two-layer structure, where the downscaled thumbnail point cloud is encoded using G-PCC at the base layer, and upon G-PCC compressed priors, multiscale sparse convolutions are stacked at the enhancement layer to effectively characterize spatial correlations to compactly represent the full-resolution sample. In addition, YOGA integrates the adaptive quantization and entropy model group to enable variable-rate control, as well as adaptive filters for better quality restoration.