ISDR-Net: Interpretable Self-Supervised Differentiable Rendering Network for monocular dynamic sensor-head pose...

Magnetoencephalography (MEG) offers high temporal and spatial resolution for clinical and neuroscience applications. Traditional sensor registration methods depend on complex point cloud reconstruction, which is error-prone, labor-intensive, and lacks adaptability. Recently, registration methods based on multi-view images have improved efficiency but still face challenges in dynamic responsiveness and speed. We propose a Interpretable Self-Supervised Differentiable Rendering Network (ISDR-Net) that enables monocular dynamic sensor-head pose tracking and registration. By integrating geometry-guided differentiable rendering with a transparent, unrolled optimization process, ISDR-Net ensures interpretability, computational efficiency, and adaptability. A coarse-to-fine optimization strategy rapidly initializes alignment, while adaptive refinement driven by keyframes and motion cues robustly tracks subtle head movements. Experimental results show that ISDR-Net aligns with the sub-millimeter registration accuracy of existing multi-view methods using only monocular single-frame images, with significantly reduced computational cost and stable performance under dynamic conditions. These results highlight the potential of ISDR-Net to support practical deployment of next-generation OPM-MEG systems in naturalistic environments.