A model of a human subject's head may be generated to assist in a therapeutic and/or diagnostic procedure. A treatment and/or diagnostic system may generate a fitted head model using a predetermined head model and a plurality of points. The plurality of points may include facial feature information and may be determined using a sensor, for example, an IR or optical sensor. One or more anatomical landmarks may be determined and registered in association with the fitted head model using the facial feature information, for example, without the use of additional image information, such as an MM image. The fitted head model may include visual aids, for example, anatomical landmarks, reference points, marking of the human subject's MT location, and/or marking of the human subject's treatment location. The visual aids may assist a technician to perform the therapeutic and/or diagnostic procedure of the human subject.

A model of a human subject's head may be generated to assist in a therapeutic and/or diagnostic procedure. A treatment and/or diagnostic system may generate a fitted head model using a predetermined head model and a plurality of points. The plurality of points may include facial feature information and may be determined using a sensor, for example, an IR or optical sensor. One or more anatomical landmarks may be determined and registered in association with the fitted head model using the facial feature information, for example, without the use of additional image information, such as an MM image. The fitted head model may include visual aids, for example, anatomical landmarks, reference points, marking of the human subject's MT location, and/or marking of the human subject's treatment location. The visual aids may assist a technician to perform the therapeutic and/or diagnostic procedure of the human subject.

An example augmented reality system includes: obtaining information about an instance of a device; recognizing the instance of the device based on the information; selecting a digital twin for the instance of the device, with the digital twin being unique to the instance of the device; and generating augmented reality content based on the digital twin and an actual graphic of the instance of the device.

An example augmented reality system includes: obtaining information about an instance of a device; recognizing the instance of the device based on the information; selecting a digital twin for the instance of the device, with the digital twin being unique to the instance of the device; and generating augmented reality content based on the digital twin and an actual graphic of the instance of the device.

A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data; and transmitting signaling data and the encoded point cloud data, wherein the step for encoding may comprise the steps of: dividing the point cloud data into a plurality of compression units; sorting, for each compression unit, the point cloud data in each compression unit; generating a prediction tree on the basis of the sorted point cloud data in the compression units; and compressing the point cloud data in the compression units by predicting on the basis of the prediction tree.

A point cloud data transmission method according to embodiments comprises the steps of: encoding point cloud data; and transmitting signaling data and the encoded point cloud data, wherein the step for encoding may comprise the steps of: dividing the point cloud data into a plurality of compression units; sorting, for each compression unit, the point cloud data in each compression unit; generating a prediction tree on the basis of the sorted point cloud data in the compression units; and compressing the point cloud data in the compression units by predicting on the basis of the prediction tree.

A three-dimensional (3D) biological cell constituent concentration reconstruction method may include capturing two-dimensional images of a biological cell at different angles, virtually partitioning the biological cell into a 3D stacks of voxels, assigning cell constituent concentration estimations to respective voxels based upon a plurality of the two-dimensional images and forming a 3D cell constituent concentration model of the biological cell based upon the voxels and respective cell constituent concentration estimations.

A three-dimensional (3D) biological cell constituent concentration reconstruction method may include capturing two-dimensional images of a biological cell at different angles, virtually partitioning the biological cell into a 3D stacks of voxels, assigning cell constituent concentration estimations to respective voxels based upon a plurality of the two-dimensional images and forming a 3D cell constituent concentration model of the biological cell based upon the voxels and respective cell constituent concentration estimations.

Provided are a reinforcement learning-based label-free six-dimensional object pose prediction method and apparatus. The method includes: obtaining a target image to be predicted, the target image being a two-dimensional image including a target object; performing pose prediction based on the target image by using a pre-trained pose prediction model to obtain a prediction result, the pose prediction model being obtained by performing reinforcement learning based on a sample image; and determining a three-dimensional position and a three-dimensional direction of the target object based on the prediction result. The pose prediction model is trained by introducing reinforcement learning, the pose prediction is performed based on the target image by using the pre-trained pose prediction model, and thus the problem of six-dimensional object pose estimation based on two-dimensional images can be solved in the absence of real pose annotation, which ensures the prediction effect of label-free six-dimensional object pose prediction.

Provided are a reinforcement learning-based label-free six-dimensional object pose prediction method and apparatus. The method includes: obtaining a target image to be predicted, the target image being a two-dimensional image including a target object; performing pose prediction based on the target image by using a pre-trained pose prediction model to obtain a prediction result, the pose prediction model being obtained by performing reinforcement learning based on a sample image; and determining a three-dimensional position and a three-dimensional direction of the target object based on the prediction result. The pose prediction model is trained by introducing reinforcement learning, the pose prediction is performed based on the target image by using the pre-trained pose prediction model, and thus the problem of six-dimensional object pose estimation based on two-dimensional images can be solved in the absence of real pose annotation, which ensures the prediction effect of label-free six-dimensional object pose prediction.