3D Point Cloud Segmentation Training with Image Alignment Constraints

The patent introduces an intermediary alignment constraint loss function that bridges the gap between scene classification supervision and point-level semantic segmentation. This loss function acts as a mediator to align point cloud features with image features, enabling accurate pseudo-label generation without requiring complex point-level annotations. The alignment constraint loss serves as the intermediary mechanism that transfers supervisory signals from the image modality to the point cloud modality, resolving the contradiction between training simplicity and labeling accuracy.

The patent implements a feedback mechanism where the alignment constraint loss continuously refines the pseudo-labels during training. The loss function provides feedback signals that adjust the point cloud feature representations to better match the image features, thereby improving the accuracy of generated pseudo-labels. This feedback loop enables the model to iteratively improve segmentation precision while maintaining the simplicity of the training process, as the feedback is automatically computed from the alignment between modalities.

The patent segments the alignment constraint computation into distinct modular components: feature alignment loss, semantic alignment loss, and structural alignment loss. Each component addresses a specific aspect of multi-modal alignment independently, allowing for efficient computation and targeted optimization. This segmentation of the alignment process reduces overall computational complexity by breaking down the complex multi-modal alignment task into manageable, specialized sub-tasks that can be computed separately and then combined.