  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.
