In point cloud geometry compression, most octreebased context models use the cross-entropy between the onehot encoding of node occupancy and the probability distribution predicted by the context model as the loss. This approach converts the problem of predicting the number (a regression problem) and the position (a classification problem) of occupied child nodes into a 255-dimensional classification problem. As a result, it fails to accurately measure the difference between the one-hot encoding and the predicted probability distribution. We first analyze why the cross-entropy loss function fails to accurately measure the difference between the one-hot encoding and the predicted probability distribution. Then, we propose an attention-based child node number prediction (ACNP) module to enhance the context models. The proposed module can predict the number of occupied child nodes and map it into an 8- dimensional vector to assist the context model in predicting the probability distribution of the occupancy of the current node for efficient entropy coding. Experimental results demonstrate that the proposed module enhances the coding efficiency of octree-based context models.

翻译：在点云几何压缩中，大多数基于八叉树的上下文模型使用节点占位独热编码与上下文模型预测的概率分布之间的交叉熵作为损失函数。该方法将预测占用子节点数量（回归问题）和位置（分类问题）的问题转化为一个255维的分类问题。因此，它无法准确度量独热编码与预测概率分布之间的差异。我们首先分析了交叉熵损失函数为何无法准确度量独热编码与预测概率分布之间的差异。随后，我们提出了一种基于注意力的子节点数量预测模块来增强上下文模型。该模块能够预测占用子节点的数量，并将其映射为一个8维向量，以辅助上下文模型预测当前节点占位的概率分布，从而实现高效熵编码。实验结果表明，所提模块有效提升了基于八叉树的上下文模型的编码效率。