Deep learning approaches have demonstrated remarkable effectiveness in point cloud geometry compression. However, existing octree-based methods face limitations due to insufficient contextual utilization within temporal sequences of dynamic point clouds. This paper proposes a spatio-temporal context model under an octree structure to enhance lossless compression of dynamic point cloud geometry. Firstly, a context extraction module is employed to capture the intra-contexts based on spatial correlations and the inter-contexts based on temporal dependencies. Subsequently, a context network employing 3D convolutional layers and fully connected layers is designed to extract spatio-temporal features from various contexts. After the context features integration, a multilayer perceptron is used to approximate the probability distribution of the occupancy symbol. The derived probability distributions finally optimize the arithmetic coding efficiency. Experimental results demonstrate that the proposed method outperforms the state-of-the-art octree-based approaches across multiple benchmark datasets.

Near-lossless compression of point clouds is suitable for the application scenarios with low distortion tolerance and certain requirements on the rate. Near-lossless attribute compression usually adopts a level-of-detail structure, where the dependencies between the layers make it possible to improve the rate-distortion (R-D) performance by using different quantization parameters for different layers. In this work, a theoretical analysis of the dependencies between adjacent layers is carried out, based on which the dependent Distortion-Quantization and Rate-Quantization models are established for point cloud attribute compression. Then an algorithm for quantization parameter cascading based on R-D optimization is proposed and implemented for near-lossless compression of point cloud attributes. The experimental results show that the proposed method has a superior performance gain compared to state-of-the-art for the Hausdorff R-D performance. At the same time, the proposed method improves subjective quality and is well adapted to various categories of point clouds.

Since the geometry constitutes most of the bitrate and is used for attribute coding, it is crucial for geometry-based point cloud compression (G-PCC). However, the current research focuses on geometry coding while ignoring reconstruction. In G-PCC, the reconstructed points are located at the center of the quantization nodes, which may not match the surface of the point clouds. Therefore, we first estimate the normal direction. Then, the offset direction of the reconstructed point is determined by considering its adjacent points’ occupancy and attributes. Finally, the position of the reconstructed point is adjusted taking into account both the normal and offset directions. The method aids in both objective and subjective quality. Experimental results demonstrate that the proposed method outperforms the state-of-the-art G-PCC. It has significant performance gains in point-to-point and point-to-plane errors, 4.5% and 9.0% on average, respectively. It also has a minor performance gain in attribute coding.

To improve the reconstructed point cloud after adaptive geometry quantization in geometry-based point cloud compression, a least squares plane (LSP) projection-based up-sampling method and a quantization parameter (QP) decision method based on loss function are proposed. First, the LSP fitting is carried out to locate the interpolated point based on the nearest neighbors of the current node during decoding, enhancing both the subjective and objective quality of the reconstructed point cloud. Second, the QP decision for each node is based on the mean squared error between the original point cloud and the reconstructed point cloud. The experimental results show that the proposed methods achieve performance gains in terms of point-to-point and point-to-plane errors for geometry by 6.3% and 1.6%, respectively, and for attributes by 1.5%, 0.7%, and 0.5%. There also has been a significant improvement in subjective quality.

Point cloud compression (PCC) is crucial for efficient and flexible storage as well as feasible transmission of point clouds in practice. For geometry compression, one popular approach is the octree-based solution. The intra prediction mechanism utilizes the spatial correlation in the static point cloud to predict the occupancy bit of the octree node for entropy coding, reducing the spatial redundancy. In this study, two local geometry-based prediction methods are proposed following statistical and theoretical analyses: binary prediction, which outputs the binary state (i.e., occupied or unoccupied), and ternary prediction, which provides a third option other than occupied or unoccupied (i.e., not predicted). In comparison to the state-of-the-art, the proposed binary prediction offers the Bjontegaard delta rate (BD-rate) of −0.8% for lossy compression and the bits per input point (bpip) of 100.09% for lossless compression in average, respectively. The binary prediction reduces the computational complexity in terms of more than 20% decrease in decoding time. In particular, it also provides noticeable reduction of the memory usage during entropy coding. The proposed ternary prediction provides −1.2% BD-rate for lossy compression and 97.19% bpip for lossless compression in average, respectively, in comparison to the state-of-the-art. While achieving performance gain, it is considerably more computational efficient by saving about 18% decoding time. Due to these advantages, part of the proposed ternary prediction has been adopted by the ongoing MPEG standard of geometry-based point cloud compression (G-PCC).

Geometry coding in geometry-based point cloud compression (G-PCC) is octree-structured, including a bitwise occupancy mode for a general case, and a single child mode for a node containing a single occupied child node. However, the current usage of the single child mode is limited because of the strict eligibility determination based on neighboring nodes. Context modeling is also missing for entropy coding of the coordinate index of the single occupied child node relative to the node. Guided by logistic regression (LR), this paper first proposes an algorithm to determine the eligibility of a node for the single child mode. Without resorting to the occupancy of the neighboring nodes, the proposed algorithm provides more opportunities for employing the single child mode. In addition, LR is also used in predicting the relative coordinate index of the single occupied child node. Based on the analysis of predicted results, we model contexts for the entropy coding of the single child mode. Experiments reveal that the proposed method improves the existing single child mode in G-PCC with overall coding gain in terms of bit per input point (bpip). Besides, the proposed method also saves coding time.