Uncertainty-Aware RL-Controlled Adaptive 3D Mapping

This work proposes an adaptive 3D mapping framework that refines voxel resolution based on scene complexity cues like semantic uncertainty, geometric curvature, and texture richness. A reinforcement learning agent controls this refinement process to optimize voxel subdivision under a memory budget, ensuring computational resources are focused on detailed areas of the scene.

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