Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation ...Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.展开更多
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 les...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 problem,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.展开更多
基金supported by the Hainan Provincial Natural Science Foundation of China(project number:621QN269)the Sanya Science and Information Bureau Foundation(project number:2021GXYL251).
文摘Augmented Reality(AR)tries to seamlessly integrate virtual content into the real world of the user.Ideally,the virtual content would behave exactly like real objects.This necessitates a correct and precise estimation of the user’s viewpoint(or that of a camera)with regard to the virtual content’s coordinate sys-tem.Therefore,the real-time establishment of 3-dimension(3D)maps in real scenes is particularly important for augmented reality technology.So in this paper,we integrate Simultaneous Localization and Mapping(SLAM)technology into augmented reality.Our research is to implement an augmented reality system without markers using the ORB-SLAM2 framework algorithm.In this paper we propose an improved method for Oriented FAST and Rotated BRIEF(ORB)feature extraction and optimized key frame selection,as well as the use of the Progressive Sample Consensus(PROSAC)algorithm for planar estimation of augmented reality implementations,thus solving the problem of increased sys-tem runtime because of the loss of large amounts of texture information in images.In this paper,we get better results by comparing experiments and data analysis.However,there are some improved methods of PROSAC algorithm which are more suitable for the detection of plane feature points.
文摘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 problem,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.
