This is the first comprehensive survey of LiDAR in rehabilitation, summarizing applications, AI techniques, trends, gaps, and future directions across studies from 2019-2025.
Pointnet: Deep learning on point sets for 3d classification and segmentation
3 Pith papers cite this work. Polarity classification is still indexing.
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ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
ML-based reconstruction improves SiW-ECAL energy resolution by about 20% at low energies and corrects high-energy leakage, enabling subsequent design reoptimization.
citing papers explorer
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LiDAR for Rehabilitation: A Comprehensive Survey of Applications, AI Techniques, and Future Directions
This is the first comprehensive survey of LiDAR in rehabilitation, summarizing applications, AI techniques, trends, gaps, and future directions across studies from 2019-2025.
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ETac: A Lightweight and Efficient Tactile Simulation Framework for Learning Dexterous Manipulation
ETac is a data-driven tactile simulation framework that matches FEM deformation accuracy at high speed, supporting 4096 parallel environments at 869 FPS and yielding 84.45% success in blind grasping across four object types.
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Optimisation of a silicon-tungsten electromagnetic calorimeter energy response to photons
ML-based reconstruction improves SiW-ECAL energy resolution by about 20% at low energies and corrects high-energy leakage, enabling subsequent design reoptimization.