An automatic markerless knee tracking and registration algorithm has been proposed in the literature to avoid the marker insertion required by conventional computer-assisted knee surgery, resulting in a shorter and less invasive surgical workflow. However, such an algorithm considers intact femur geometry only. The bone surface modification is inevitable due to intra-operative intervention. The mismatched correspondences will degrade the reliability of registered target pose. To solve this

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... em, this work proposed a supervised deep neural network to automatically restore the surface of processed bone. The network was trained on a synthetic dataset that consists of real depth captures of a model leg and simulated realistic femur cutting. According to the evaluation on both synthetic data and real-time captures, the registration quality can be effectively improved by surface reconstruction. The improvement in tracking accuracy is only evident over test data, indicating the need for future enhancement of the dataset and network.