In this paper, we propose a deep hierarchical attention context model for lossless attribute compression of point clouds, leveraging a multi-resolution spatial structure and residual learning. A simple and effective Level of Detail (LoD) structure is introduced to yield a coarse-to-fine representation. To enhance efficiency, points within the same refinement level are encoded in parallel, sharing a common context point group. By hierarchically aggregating information from neighboring points, our attention model learns contextual dependencies across varying scales and densities, enabling comprehensive feature extraction. We also adopt normalization for position coordinates and attributes to achieve scale-invariant compression. Additionally, we segment the point cloud into multiple slices to facilitate parallel processing, further optimizing time complexity. Experimental results demonstrate that the proposed method offers better coding performance than the latest G-PCC for color and reflectance attributes while maintaining more efficient encoding and decoding run-times.

Point clouds serve as a fundamental format for virtual and augmented reality applications. However, their substantial data volumes present considerable challenges regarding data storage and transmission. This paper introduces a novel point transformer-based approach for point cloud attribute compression, termed as CST-PCAC, which utilizes a transformer architecture designed to exploit correlations across multiple scales. Our method is grounded in a variational autoencoder framework, enhanced by stacked sparse convolution (SConv) and the proposed Cross-Scale Point Transformer (CSFormer). CSFormer incorporates two types of attention mechanisms: Within-Scale Attention (WSA) and Cross-Scale Attention (CSA). WSA constructs attention maps within K-nearest neighbours at the current scale, while CSA operates across adjacent upper or lower scales. By leveraging varied sparsity distributions and the geometry distortions inherent in consecutive scales, CSFormer enhances the receptive field and feature representation, thereby effectively extracting correlations to minimize redundancy. Experimental results demonstrate that CST-PCAC achieves average Bjøntegaard Delta bitrate gains exceeding 30%, 10%, and 20% in comparison to Sparse-PCAC, Scalable-PCAC, and G-PCC, respectively. In terms of computational efficiency, CST-PCAC exhibits a time complexity two orders of magnitude lower than that of deep learning-based methods including Sparse-PCAC and Scalable-PCAC, while maintaining a comparable complexity to traditional methods like G-PCC.

Blendshapes have been widely adopted as a key method for generating facial animation on avatars due to their ease of manipulation, flexibility in capturing diverse facial expressions, and compatibility with real-time rendering. However, current frameworks lack efficient methods for compressing blendshape (BS) animation parameters, which are critical for optimizing data transmission. This study introduces a pioneer compression scheme leveraging the amount of BS to be transmitted, their quantization as well as their transmission frequency. Subjective evaluation using the ITU-R BT.500-15 recommendation demonstrates that the proposed method significantly reduces the amount of data to transmit while preserving acceptable visual quality. This approach extends prior findings on reduced BS sets[kang2023effects] and addresses a significant gap in avatar media coding[avril2023morgan]. This work establishes a baseline for efficient facial animation and, serves as a foundation for further exploration on adaptive rate-allocation strategies and advanced compression strategies tailored for diverse avatar animation scenarios.

This paper investigates the impact of standardized Geometry-based Point Cloud Compression (G-PCC) on LiDAR-based object detection tasks. It evaluates how compression distortion types, and distortion levels, together with point cloud density, and object semantics affect various 3D object detection methods, including point-based, voxel-based, and hybrid approaches, using the KITTI object detection dataset.

The continuous evolution of cloud gaming makes it crucial for efficient video compression to relieve the growing bandwidth pressure. This paper presents a coding optimization algorithm for cloud gaming videos based on three-dimensional image warping, considering the significance of reference frames in inter-frame prediction. By leveraging camera parameters and motion information from the game engine, we generate a virtual reference frame through three-dimensional image warping on the selected key frame and the coloring operation. The resulting virtual reference frame exhibits minimal spatial differences from the frame to be encoded and is utilized to update the original reference frame list in two different methods, achieving accurate inter-frame prediction and significantly reducing coding residuals. Experimental results demonstrate the superiority of our algorithm compared to the mainstream cloud gaming encoder in various camera movements, achieving an average BD-rate reduction of 5.60% and BD-PSNR gain of 0.55dB.

We present an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. It involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.