Du-FreqNet generates controllable depth-dependent microrobot microscopy images via dual ControlNet branches and frequency-aware loss, improving SSIM by 20.7% over baselines and aiding pose estimation.
A dataset and benchmarks for deep learning- based optical microrobot pose and depth perception,
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The paper introduces micro-dexterity as a framework for biological micromanipulation by reformulating classical primitives in fluidic, surface-dominated micro-environments and comparing contact-based, field-mediated, and multi-agent architectures.
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Dual-Control Frequency-Aware Diffusion Model for Depth-Dependent Optical Microrobot Microscopy Image Generation
Du-FreqNet generates controllable depth-dependent microrobot microscopy images via dual ControlNet branches and frequency-aware loss, improving SSIM by 20.7% over baselines and aiding pose estimation.
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Micro-Dexterity in Biological Micromanipulation: Embodiment, Perception, and Control
The paper introduces micro-dexterity as a framework for biological micromanipulation by reformulating classical primitives in fluidic, surface-dominated micro-environments and comparing contact-based, field-mediated, and multi-agent architectures.