3D Terrain–Object Model Separation from Multi-Viewpoint Images

The patent segments the separation process into multiple automated stages: initial classification using machine learning models, refinement through iterative optimization, and final validation. This segmentation enables high-accuracy separation without requiring complete manual intervention, as each stage handles specific aspects of the separation task automatically.

The system performs self-service by using the input images and extracted features to automatically guide the separation process. The machine learning models self-adjust parameters and make decisions about terrain-object boundaries without human intervention, enabling the system to serve itself in achieving accurate separation while minimizing time consumption.

The patent replaces the mechanical manual separation process with an automated computational system. Machine learning models and algorithms substitute human operators, performing terrain-object separation through automated image analysis, feature extraction, and classification. This substitution eliminates labor costs while maintaining or improving separation accuracy through consistent, repeatable automated processes.

The system achieves self-service by automatically processing images and making separation decisions without human labor. The machine learning models self-adjust and self-optimize the separation process, eliminating the need for manual intervention and associated labor costs while maintaining high separation accuracy through automated decision-making algorithms.

The patent segments the integrated model into distinct terrain and object components automatically. By separating terrains and objects into different data structures and processing streams, the system enables independent manipulation and optimization of each component type, significantly improving usability for applications that require selective editing, analysis, or rendering of specific elements.

The system implements dynamic separation where the level of detail and separation granularity can be adjusted based on application requirements. The separation process is not static but can be optimized dynamically, allowing users to control the degree of separation and detail according to specific usability needs while maintaining processing efficiency through adaptive algorithms.

The patent segments terrains and objects into separate model components with distinct geometric and textural properties. This segmentation preserves the realistic characteristics of each element type by allowing independent optimization of their respective models, maintaining high realism for applications requiring accurate representation of natural terrains and man-made or natural objects.

The system applies local quality by treating terrains and objects with different modeling approaches and detail levels appropriate to their specific characteristics. Terrains receive processing optimized for natural surfaces while objects receive processing suited to their geometric properties, ensuring each element maintains its realistic appearance and properties while the overall process remains efficient through specialized handling.