3D Neural Implicit Surface Reconstruction for Dense HD Maps

The patent replaces traditional geometric processing methods with neural implicit surface networks that use continuous function representations. Instead of relying on discrete point cloud data and manual semantic segmentation, the system employs differentiable neural networks to implicitly represent 3D environments, enabling dense HD map generation with rich appearance information while maintaining computational efficiency through gradient-based optimization.

The patent combines multiple sensor modalities (RGB images, depth maps, semantic labels) into a unified neural representation framework. By fusing heterogeneous data types and representing them through a composite neural implicit surface model, the system achieves high-resolution dense HD maps that integrate geometric, photometric, and semantic information in a coherent framework.

The patent implements self-supervised learning where the neural implicit surface network automatically learns semantic labels from raw sensor data without requiring manual annotation. The system uses unsupervised feature learning and self-supervised contrastive learning to generate accurate semantic segmentations, eliminating the need for time-consuming manual labeling while maintaining high labeling accuracy through automated feature extraction and representation learning.

The patent performs preliminary feature extraction and representation learning during the map generation process itself, rather than requiring separate manual labeling steps. By pre-training the neural network on large datasets and using transfer learning, the system prepares the semantic understanding capabilities in advance, enabling rapid and accurate semantic segmentation during actual HD map generation without post-processing manual intervention.

The patent transitions from representing environments in discrete 3D space (point clouds) to continuous function space using neural implicit surfaces. By encoding environmental information as continuous differentiable functions rather than discrete data structures, the system enables efficient querying and rendering at any resolution, reducing computational energy requirements while maintaining high perception and localization accuracy through smooth gradient-based optimization.

The patent changes the fundamental parameters of environmental representation from discrete geometric features to continuous neural field parameters. By using learnable neural network parameters to define spatial distributions of geometric, photometric, and semantic properties, the system achieves dense HD map representation with rich information while enabling efficient inference and rendering through parameter-based querying rather than exhaustive data processing.