3D Feature-Embedded Semantic Segmentation for Image Components

The patent applies preliminary action by performing depth map generation and three-dimensional feature extraction before the main segmentation process. The depth information is pre-processed and integrated into the feature pyramid structure, allowing the segmentation network to receive enriched features without adding complexity during the core segmentation operation. This pre-computation of 3D features from 2D images and depth maps resolves the contradiction by preparing the computational work in advance.

The patent implements dimensionality change by transitioning from traditional two-dimensional image features to three-dimensional feature representations. By incorporating depth information and constructing 3D feature pyramids, the system adds a third dimension to the feature space, enabling more accurate segmentation while managing computational complexity through efficient 3D convolution operations and feature fusion strategies.

The patent applies merging by integrating 2D image features and 3D depth features into a unified feature pyramid structure. The multi-scale feature fusion network combines features from different sources and scales simultaneously, allowing parallel processing of multiple feature types. This fusion approach improves segmentation accuracy while managing processing time by consolidating operations into a single integrated network rather than sequential processing.

The system performs preliminary extraction and preparation of both 2D and 3D features before the main segmentation task. Depth maps are pre-computed from images, and feature pyramids are pre-constructed at multiple scales, enabling the segmentation network to operate with enriched features without incurring excessive processing delays during the critical segmentation phase.