Regional Adaptive Hierarchical Transform (RAHT) is an effective point cloud attribute compression (PCAC) method. However, its application in deep learning lacks research. In this paper, we propose an end-to-end RAHT framework for lossy PCAC based on the sparse tensor, called DeepRAHT. The RAHT transform is performed within the learning reconstruction process, without requiring manual RAHT for pre-processing. We also introduce the predictive RAHT to reduce bitrates and design a learning-based prediction model to enhance the performance. Moreover, we devise a bitrate proxy that applies run-length coding to entropy model, achieving seamless variable-rate coding and improving the robustness. DeepRAHT is a reversible and distortion-controllable framework, ensuring its lower bound performance and offering significant application potential. The experiments demonstrate that DeepRAHT is a high-performance, faster, and more robust solution than the baseline methods.

Substantial Gaussian splatting format point clouds require effective compression. In this paper, we propose Voxel-GS, a simple yet highly effective framework that departs from the complex neural entropy models of prior work, instead achieving competitive performance using only a lightweight rate proxy and run-length coding. Specifically, we employ a differentiable quantization to discretize the Gaussian attributes of Scaffold-GS. Subsequently, a Laplacian-based rate proxy is devised to impose an entropy constraint, guiding the generation of high-fidelity and compact reconstructions. Finally, this integer-type Gaussian point cloud is compressed losslessly using Octree and run-length coding. Experiments validate that the proposed rate proxy accurately estimates the bitrate of run-length coding, enabling Voxel-GS to eliminate redundancy and optimize for a more compact representation. Consequently, our method achieves a remarkable compression ratio with significantly faster coding speeds than prior art. The code is available at https://github.com/zb12138/VoxelGS.

Recently, deep learning has significantly advanced the performance of point cloud geometry compression. However, the learning-based lossless attribute compression of point clouds with varying densities is under-explored. In this article, we develop a learning-based framework, namely DALD-PCAC that leverages Levels of Detail (LoD) to tailor for point cloud lossless attribute compression. We develop a point-wise attention model using a permutation-invariant Transformer to tackle the challenges of sparsity and irregularity of point clouds during context modeling. We also propose a Density-Adaptive Learning Descriptor (DALD) capable of capturing structure and correlations among points across a large range of neighbors. In addition, we develop a prior-guided block partitioning to reduce the attribute variance within blocks and enhance the performance. Experiments on LiDAR and object point clouds show that DALD-PCAC achieves the state-of-the-art performance on most data. Our method boosts the compression performance and is robust to the varying densities of point clouds. Moreover, it guarantees a good tradeoff between performance and complexity, exhibiting great potential in real-world applications. The source code is available at https://github.com/zb12138/DALD_PCAC .

For mesh animation sequences with consistent topology, traditional compression methods are well established, but deep learning approaches are emerging. This paper presents a novel deep learning framework for vertex position reconstruction by encoding displacements. We propose LoD-Uniform Partitioning (LUP), which uses Levels-of-Detail (LoD) partitioning and uniform sampling to partition vertices into base and inference layers. Base layers use vertex-wise prediction and run-length coding, while inference layers use inter-layer prediction and a deep entropy model with a permutation-invariant transformer. By integrating vertex-based, face-based, and trajectory-based descriptors into our model context, we improve coding efficiency. Experiments on mesh animation datasets show that our method achieves a 25% bitrate reduction for lossless compression, a 15% BD-Rate improvement, and a 95% reduction in encoding time for lossy compression compared to MPEG Video-based Dynamic Mesh Compression (V-DMC), underscoring its strong potential for practical applications.

Learning-based lossy point cloud compression has garnered significant attention recently. However, current methods have insufficient exploration of entropy models, especially in the inter-group context. This paper proposes an innovative method called GroupAC for point cloud attribute compression by leveraging inter-group context. Initially, point cloud attributes are transformed into coefficients. We then develop a deep entropy model incorporating inter-group context to estimate the probability distribution of these coefficients. The deep entropy model splits the coefficients into groups, wherein the encoding groups utilize context from preceding groups to enhance probability distribution modeling. Finally, an arithmetic encoder compresses the coefficients into a bitstream based on the estimated probability distribution. Experimental results on indoor and outdoor point cloud datasets, including ScanNet and SemanticKITTI, demonstrate that our approach outperforms MPEG G-PCC (TMC13v23) and existing learning-based methods.

Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression. Recently, the octree-based auto-regressive entropy model which adopts the self-attention mechanism to explore dependencies in a large-scale context is proved to be promising. However, heavy global attention computations and auto-regressive contexts are inefficient for practical applications. To improve the efficiency of the attention model, we propose a hierarchical attention structure that has a linear complexity to the context scale and maintains the global receptive field. Furthermore, we present a grouped context structure to address the serial decoding issue caused by the auto-regression while preserving the compression performance. Experiments demonstrate that the proposed entropy model achieves superior rate-distortion performance and significant decoding latency reduction compared with the state-of-the-art large-scale auto-regressive entropy model.

In this paper, we prove that it is feasible to perform mesh quality assessment by sampling it into point cloud. We propose a general and efficient surface-sampling based framework that can deal with various types and levels of distortions with less complexity. In this method, the original and distorted meshes are first converted into point clouds by sampling the triangle surfaces. Then, the geometry and attribute quality of the distorted mesh can be evaluated by the well-defined point cloud quality metrics. The final objective score can be obtained by fusing multiple quality metrics to get a more accurate prediction of the subjective quality. In addition, we compare the performance in terms of different sampling methods and sampling resolutions on a large public dataset, thus being able to suggest the best sampling configurations.

In octree-based point cloud compression, an effective entropy model is required to reduce the final code length. The large-scale context provides more references and improves the accuracy of the entropy coder. In this paper, we propose a learning-based entropy model to exploit the large-scale spatio-temporal context for dynamic point cloud compression. We design an octree-based context structure which substantially expands the context. To extract powerful features from the informative large-scale context, we propose a geometry-aware graph-based feature extraction model. Furthermore, we present a spatio-temporal attention mechanism to discover dependencies within the large-scale context. Extensive experiments demonstrate that the proposed method achieves state-of-the-art compression performance.

In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution. However, the contexts gathered by the previous voxel-based methods decrease when handling sparse point clouds. To address this problem, we propose a multiple-contexts deep learning framework called OctAttention employing the octree structure, a memory-efficient representation for point clouds. Our approach encodes octree symbol sequences in a lossless way by gathering the information of sibling and ancestor nodes. Expressly, we first represent point clouds with octree to reduce spatial redundancy, which is robust for point clouds with different resolutions. We then design a conditional entropy model with a large receptive field that models the sibling and ancestor contexts to exploit the strong dependency among the neighboring nodes and employ an attention mechanism to emphasize the correlated nodes in the context. Furthermore, we introduce a mask operation during training and testing to make a trade-off between encoding time and performance. Compared to the previous state-of-the-art works, our approach obtains a 10%-35% BD-Rate gain on the LiDAR benchmark (e.g. SemanticKITTI) and object point cloud dataset (e.g. MPEG 8i, MVUB), and saves 95% coding time compared to the voxel-based baseline. The code is available at https://github.com/zb12138/OctAttention.