We propose Next Bit Prediction (NBP), a unified framework that simultaneously addresses lossless compression and lossy reconstruction of 3D point cloud geometry through a next-bit probability estimation paradigm. Our key insight is that both lossless compression and lossy reconstruction fundamentally rely on accurate probability estimation of geometric symbols, though targeting different metrics. Lossless compression minimizes bitrate via precise symbol distribution prediction, while lossy reconstruction enhances reconstruction fidelity through probability-guided geometry refinement. Recognizing that point clouds become sparser with increasing bit depth, NBP introduces two key technical innovations. For more significant bits, where the point density is higher, we develop a multi-stage Occupancy Probability Estimation (OPE) mechanism to estimate the probability distribution of occupancy status across multiple iteration stages, with each stage supporting either lossless or lossy mode. For less significant bits that focus on point placement, a Disentangled Probability Estimation (DPE) module is proposed to handle density information and binary residuals, simultaneously enabling lossless compression and facilitating probability-driven coordinate refinement for high-quality lossy reconstruction. Extensive experiments demonstrate the advantages of NBP, including low complexity, progressive coding, and superior coding efficiency, achieving state-of-the-art results both quantitatively and qualitatively.

Voxel-based methods are among the most efficient for point cloud geometry compression, particularly with dense point clouds. However, they face limitations due to a restricted receptive field from the upsampling operation, especially when handling high-bit-depth point clouds. To overcome this issue, we introduce a stage-wise Space-to-Channel (S2C) context model for both dense point clouds and the low-level part of sparse point clouds. This model utilizes a channel-wise autoregressive strategy to effectively integrate neighborhood information at a coarse resolution. For the high-level part of sparse point clouds, we further propose a level-wise S2C context model that addresses resolution limitations by incorporating Geometry Residual Coding (GRC) for consistent-resolution cross-level prediction. Additionally, we use the spherical coordinate system for its compact representation and enhance our GRC approach with a Residual Probability Approximation (RPA) module, which features a large kernel size. Experimental results show that our S2C context model not only achieves bit savings while maintaining or improving reconstruction quality but also reduces computational complexity compared to state-of-the-art voxel-based compression methods.

Joint geometry-attribute point cloud compression is formulated to compress both geometry coordinates and their corresponding attributes within a single model, thereby yielding the benefit of reduced computation and enhancing the feasibility of joint geometry-attribute optimization. However, previous single-model approaches have been hampered by inadequate utilization of local context, thereby retaining spatial redundancy. To effectively characterize the spatial correlation conditioned on the neighborhood, we propose a novel spatial context model that features group division for two-stage parallel processing and incorporates a local attention-based spatial context mining module. Furthermore, for joint compression tasks, geometry distortion results in the absence of references for misclassified voxels, consequently preventing one-to-one matching between the reference and the reconstruction for attribute loss computation. To address this issue, we propose a dual-class attribute loss that enhances guidance by searching for the nearest neighbors of misclassified voxels and utilizing them as references. Experiments demonstrate that the proposed joint compressor achieves state-of-the-art rate-distortion performance compared to existing single-model methods and yields a competitive result against multiple-model solutions.