Lightweight Depth-Based Tracking with Memory-Efficient Surface Encoding for Mobile Augmented Reality Surgical Platforms
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Abstract
Mobile AR surgical systems face strict computational and memory constraints. This study presents a lightweight markerless tracking algorithm based on compressed surface encoding and incremental pose update strategies. Surface representations are quantized using a vector-clustering scheme, reducing memory usage by 31.4% compared with full-resolution meshes. Incremental pose estimation is performed using a local depth residual model that avoids global re-optimization. Testing was conducted on 50 simulated surgical sequences (total 18,000 frames). Average tracking drift over 2 minutes was limited to 1.21 mm, while maintaining 52 FPS on a mobile AR processor. Compared to standard dense ICP pipelines, computation time decreased by 38.9% without significant loss in accuracy. This approach is suitable for battery-powered head-mounted surgical devices.
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