Stereo Matching Algorithm Based on Belief Propagation

This article discusses a stereo matching algorithm based on belief propagation, which is used for 3D imaging applications. The implementation employs max-product belief propagation combined with accelerated updating techniques to enhance both accuracy and computational efficiency, making it suitable for broader applications. The max-product belief propagation algorithm operates by iteratively passing messages between neighboring nodes to maximize the joint probability distribution, typically implemented using message passing schedules that update belief estimates for each pixel's disparity. The accelerated updating technique improves convergence speed through optimized message scheduling and damping factors, allowing faster stabilization of belief estimates. We will provide detailed explanations of these techniques and demonstrate their integration into stereo matching pipelines, including code structure for message initialization, belief updating, and disparity selection from maximum posterior estimates. This approach yields more accurate 3D reconstruction results while maintaining practical computation times.