3D Training Image Generation for Low-Latency Deep Learning

The patent combines computer vision processing and graphics rendering into a single unified pipeline that shares memory resources and processing stages. The voxel data structure serves both vision and rendering purposes simultaneously, eliminating the need for separate subsystems while maintaining parallel processing capabilities through efficient memory sharing between different processing stages.

The patent applies different levels of voxel resolution to different spatial regions based on their importance. High-resolution voxels are used only in regions containing relevant objects or features, while lower-resolution or empty voxels are used in irrelevant regions. This localized quality adjustment maintains measurement precision where needed while dramatically reducing overall memory requirements for the volumetric data structure.

The patent segments the volumetric data processing into discrete, independently processable voxel operations that can be executed in parallel. By breaking down complex 3D scene operations into individual voxel-level tasks (such as occupancy testing, ray traversal, and rendering operations), the system achieves real-time processing speeds while minimizing latency through efficient memory access patterns and parallel execution of segmented operations.