论文标题
学习在OCTREE上预测可扩展点云几何编码
Learning to Predict on Octree for Scalable Point Cloud Geometry Coding
论文作者
论文摘要
MPEG G-PCC标准已经采用了基于OCTREE的点云表示和压缩。但是，它仅使用手工制作的方法来预测叶子节点是非空的概率，然后将其用于熵编码。我们提出了一种新的方法来预测几何形状编码的概率，该方法将神经网络应用于“嘈杂”上下文立方体，其中包括相邻的解码体素和未编码的素。我们进一步提出了一个基于卷积的模型，以在解码器侧的粗分辨率上为解码点云进行采样。与原始的G-PCC标准和其他基线方法相比，两种方法的集成显着提高了几何形状编码的速率延伸性能。提出的基于OCTREE的熵编码方法是自然可扩展的，这对于点云流系统中的动态速率适应性是可取的。
Octree-based point cloud representation and compression have been adopted by the MPEG G-PCC standard. However, it only uses handcrafted methods to predict the probability that a leaf node is non-empty, which is then used for entropy coding. We propose a novel approach for predicting such probabilities for geometry coding, which applies a denoising neural network to a "noisy" context cube that includes both neighboring decoded voxels as well as uncoded voxels. We further propose a convolution-based model to upsample the decoded point cloud at a coarse resolution on the decoder side. Integration of the two approaches significantly improves the rate-distortion performance for geometry coding compared to the original G-PCC standard and other baseline methods for dense point clouds. The proposed octree-based entropy coding approach is naturally scalable, which is desirable for dynamic rate adaptation in point cloud streaming systems.
