用体素与点云混合上下文，显著降低点云压缩码率。
PVContext: Hybrid Context Model for Point Cloud Compression
- 融合体素局部几何与点云全局形状信息，构建双模上下文
- 在SemanticKITTI上比G-PCC降低37.95%码率
- 适合高精度点云压缩场景，尤其适用于大范围结构保留
由于扫描技术的进步，大规模点云数据的高效存储变得日益困难。近年来深度学习方法彻底改变了该领域，但大多数现有方法依赖单一模态上下文（如八叉树节点或体素占据），难以捕捉大范围信息。本文提出PVContext，一种用于八叉树点云压缩的混合上下文模型。该模型包含两个不同模态的组件：体素上下文利用体素精确表示局部几何信息，点上下文则高效保留点云的全局形状特征。通过融合两者，既保持了大区域细节，又控制了上下文规模。该联合上下文输入深度熵模型以准确预测占据情况。实验结果表明，相较于G-PCC，在SemanticKITTI LiDAR点云上码率降低37.95%；在MPEG 8i和MVUB的密集物体点云上分别降低48.98%和36.36%。
原文摘要 · Abstract (English)
Efficient storage of large-scale point cloud data has become increasingly challenging due to advancements in scanning technology. Recent deep learning techniques have revolutionized this field; However, most existing approaches rely on single-modality contexts, such as octree nodes or voxel occupancy, limiting their ability to capture information across large regions. In this paper, we propose PVContext, a hybrid context model for effective octree-based point cloud compression. PVContext comprises two components with distinct modalities: the Voxel Context, which accurately represents local geometric information using voxels, and the Point Context, which efficiently preserves global shape information from point clouds. By integrating these two contexts, we retain detailed information across large areas while controlling the context size. The combined context is then fed into a deep entropy model to accurately predict occupancy. Experimental results demonstrate that, compared to G-PCC, our method reduces the bitrate by 37.95\% on SemanticKITTI LiDAR point clouds and by 48.98\% and 36.36\% on dense object point clouds from MPEG 8i and MVUB, respectively.
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