Monocular Camera Depth Estimation Using Bokeh Statistical Models

The patent introduces an intermediary statistical model that acts as a mediator between the monocular camera and the final distance estimation. The model learns the relationship between bokeh characteristics and distance, providing reliable depth information in regions where direct estimation fails, thus resolving the contradiction between simple device installation and precise measurement.

The patent creates a virtual depth map through statistical modeling that copies the depth information pattern learned from training data. This virtual depth map serves as a substitute for direct physical measurement, enabling accurate distance estimation in regions where the monocular camera alone would fail, while maintaining the simplicity of the monocular setup.

The patent replaces the mechanical LiDAR system with a computational approach using statistical models and image processing. Instead of using active light measurement hardware, the system substitutes a learned statistical relationship between bokeh and distance, achieving similar precision goals without the complexity and cost of LiDAR hardware integration.

The patent uses a computationally lightweight statistical model that can be trained once and then efficiently applied to multiple images. This approach is more economical and simpler than continuously operating LiDAR hardware, providing a cost-effective solution that maintains measurement precision without requiring expensive, complex distance measuring devices.