Cascaded Multi-Stage Disparity Estimation for Accuracy and Speed

The patent segments the disparity estimation process into multiple stages with different resolution levels. The first stage processes images at a lower resolution to generate an initial disparity map quickly, while subsequent stages process at higher resolutions for refinement. This segmentation allows the system to achieve high measurement precision through multi-scale processing while reducing overall energy consumption by avoiding computationally intensive global optimization at all resolution levels simultaneously.

The patent applies preliminary action by performing disparity estimation at a lower resolution first before proceeding to higher resolutions. The first stage generates an initial disparity map at reduced resolution, which then serves as input for subsequent refinement stages. This preliminary low-resolution processing provides a good starting point that reduces the computational burden in later stages, effectively balancing precision requirements with energy consumption.

The patent segments the neural network processing into multiple stages with varying computational complexity. The first stage uses a simpler neural network architecture processing lower-resolution images, while subsequent stages use more complex architectures for higher-resolution refinement. This segmentation enables the system to achieve high measurement precision through progressive refinement while managing device complexity by not deploying the most complex network at all stages simultaneously.

The patent applies partial action by using different levels of network complexity appropriate to each stage's requirements. Rather than consistently using the most complex neural network architecture, the system uses simpler networks for initial low-resolution processing and progressively applies more complex networks only when higher precision is needed at higher resolutions. This partial application of computational resources balances measurement precision with device complexity.

The patent segments the disparity estimation process into multiple resolution stages, where the first stage uses local area matching methods at reduced resolution to achieve high productivity and fast processing. Subsequent stages then perform refinement at higher resolutions to improve measurement precision. This segmentation allows the system to benefit from the speed of local matching methods while still achieving the accuracy of more comprehensive methods through multi-scale processing.

The patent applies preliminary action by performing fast local area matching at lower resolution first to generate an initial disparity map quickly. This preliminary processing achieves high productivity and provides a good initial result that can be refined in subsequent stages. By doing the computationally cheap low-resolution matching first, the system achieves fast processing speeds while still having the opportunity to improve precision through later refinement stages.

The patent implements universality by creating a multi-stage disparity processing system that can serve multiple functions and performance requirements simultaneously. The system generates disparity maps at multiple resolutions, allowing it to adapt to different application needs - from real-time low-precision requirements to high-precision requirements. This multi-functional architecture provides adaptability to various performance requirements while maintaining a unified processing framework that manages complexity effectively.

The patent applies dynamics by making the processing architecture adaptable through multiple stages that can be activated based on performance requirements. The system dynamically adjusts the resolution and computational intensity of processing stages depending on the specific application needs. This dynamic multi-stage design provides versatility for different accuracy-speed trade-off requirements while managing device complexity through a structured hierarchical approach.