In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the probability distribution predicted by the context model as the loss function. However, this approach has two main weaknesses. First, the differences between contexts of different nodes are not significant, making it difficult for the context model to accurately predict the probability distribution of node occupancy. Second, as the one-hot encoding is not the actual probability distribution of node occupancy, the cross-entropy loss function is inaccurate. To address these problems, we propose a general structure that can enhance existing context models. We introduce the context feature residuals into the context model to amplify the differences between contexts. We also add a multi-layer perception branch, that uses the mean squared error between its output and node occupancy as a loss function to provide accurate gradients in backpropagation. We validate our method by showing that it can improve the performance of an octree-based model (OctAttention) and a voxel-based model (VoxelDNN) on the object point cloud datasets MPEG 8i and MVUB, as well as the LiDAR point cloud dataset SemanticKITTI.

通过引入上下文特征残差和多层感知分支，我们提出了一种改进现有上下文模型的通用结构，在点云几何压缩中提高了性能。我们验证了该方法在MPEG 8i、MVUB物体点云数据集以及LiDAR点云数据集SemanticKITTI上对基于八叉树的模型（OctAttention）和基于体素的模型（VoxelDNN）的性能改进。

利用上下文特征残差和多损失增强点云几何压缩的上下文模型