With the rapid advancement of Augmented Reality (AR) and autonomous driving, the storage and transmission of high-density point clouds have become critical challenges. To address this, we propose HMD-PCGC, a novel end-to-end Point Cloud Geometry Compression framework that synergizes a Mamba-Diffusion architecture with an adaptive hybrid representation learning mechanism. Unlike traditional approaches that rely on single-modality inputs, our encoder integrates three complementary feature types—point-based, voxel-based, and implicit features—through a Cross-Representation Attention module. This design allows for the comprehensive capture of local geometric details and complex topologies. To effectively model long-range dependencies within the latent space without the computational bottleneck of Transformers, we employ a Mamba block (State Space Model) for sequential processing, coupled with a scorer-guided adaptive quantization module that dynamically allocates bit-rate based on feature significance. For the decoding stage, we utilize a conditional Diffusion Model that generates high-fidelity point clouds by reversing a noising process, conditioned on the compressed latent representation. Extensive experiments on the MPEG 8iVFB and MVUB datasets demonstrate that HMD-PCGC achieves significant improvements in compression efficiency. Specifically, our method yields average BD-Rate gains exceeding 90% against G-PCC (octree), 80% against G-PCC (trisoup), and 40% against V-PCC, while also outperforming several recent learning-based frameworks in both rate-distortion efficiency and visual reconstruction quality.

Despite the efficiency of lossy geometry-based point cloud compression, decompressed point clouds exhibit distortions that depend on the compression level. Severe distortions, in particular, highlight the need for geometry restoration. Existing methods are limited in practice: they train separate models for specific distortion levels and require prior knowledge of these levels at inference, preventing generalization to broader distortion cases. To address these issues, we propose GGRNet, a generalizable geometry restoration network for compressed point clouds. GGRNet trains a single model and requires no prior knowledge of distortion levels for inference. Specifically, it adopts a sparse encoder-decoder for hierarchical geometry reconstruction. To handle diverse point densities and distortions, we introduce distortion-aware mixture-of-experts descriptors, which adaptively capture global and local features via dynamic gating. We further equip the decoder with distortion prompt adapters to implicitly encode and adapt to different distortion levels. Finally, we design a perceptual alignment loss to ensure perceptual invariance in the learned prompts. Extensive experiments show that GGRNet achieves strong performance in both the geometry restoration and downstream analysis tasks, demonstrating practical potential for real-world applications.

With the rapid growth in the scale of point cloud data, lossy point cloud compression has become increasingly important for data storage and transmission. However, most existing point cloud compression methods assume that the input point clouds are noise-free. In practical applications, point clouds captured by 3D scanners or LiDAR sensors are often affected by noise, which significantly hinders subsequent point cloud processing and analysis. When applying existing compression algorithms directly to noisy point clouds, their performance deteriorates drastically. To address this issue, this paper explores a more challenging yet practical task: lossy geometry compression for point clouds with various noise levels. In this paper, we propose a novel approach, termed PCGCD, which jointly optimizes point cloud geometry compression and denoising. We first design a learning strategy for joint compression and denoising, enabling the basic mapping process from “noisy” point clouds to “clean” ones. Furthermore, we introduce a Prompt Guidance Module (PGM) embedded in the decoder to guide the reconstruction of high-quality point clouds, achieving noise-adaptive decoding and denoising. Extensive experiments conducted in both synthetic and real-world noise scenarios validate the effectiveness of our approach, showing significant improvements compared to baseline methods.

Dynamic point clouds serve as crucial representations of three-dimensional moving entities across diverse applications. The substantial amount of data in point clouds necessitates the development of efficient compression techniques. Motion estimation (ME) plays a crucial role in eliminating the temporal redundancy of point cloud sequences. However, prevailing ME methods suffer from the inaccurate geometry distortion measure and the imbalanced rate-distortion modeling, significantly impacting the coding performance. To address these challenges, we propose a rate-distortion (R-D) optimized ME scheme for dynamic point cloud geometry compression.