A system for training a machine learning framework to estimate depths of objects captured in 2-D images includes a first trained machine learning network and a second untrained or minimally trained machine learning framework. The first trained machine learning network is configured to analyze 2-D images of target spaces including target objects and to provide output indicative of 3-D positions of the target objects in the target spaces. The second machine learning network can be configured to provide an output responsive to receiving a 2-D input image. A comparator receives the outputs from the first and second machine learning networks based on a particular 2-D image. The comparator compares the output of the first trained machine learning network with the output of the second machine learning network. A feedback mechanism is operative to alter the second machine learning network based at least in part on the output of the comparator.

A distance measurement accuracy is improved without increasing power consumption of an image processing apparatus that performs distance-measuring processing. In one embodiment, an image processing apparatus for calculating distance information on an image has a reliability calculation unit 113 configured to calculate reliability in accordance with contrast for each pixel of the image and a distance calculation unit 116 configured to calculate distance information on each of the pixels based on reliability of each of the pixels. The distance calculation unit 116 calculates the distance information about a second pixel group whose reliability is lower than that of a first pixel group by using a collation area whose size is larger than a predetermined size in a range in which an amount of calculation in a case where a collation area of the predetermined size is used for all the pixels of the image is not exceeded.

A distance measurement accuracy is improved without increasing power consumption of an image processing apparatus that performs distance-measuring processing. In one embodiment, an image processing apparatus for calculating distance information on an image has a reliability calculation unit 113 configured to calculate reliability in accordance with contrast for each pixel of the image and a distance calculation unit 116 configured to calculate distance information on each of the pixels based on reliability of each of the pixels. The distance calculation unit 116 calculates the distance information about a second pixel group whose reliability is lower than that of a first pixel group by using a collation area whose size is larger than a predetermined size in a range in which an amount of calculation in a case where a collation area of the predetermined size is used for all the pixels of the image is not exceeded.

Disclosed are an apparatus and a method for controlling a lane change using V2V communication information and an apparatus for calculating tendency information for the same. According to the apparatuses and the method, it is possible to improve safety when changing lanes by receiving diving information of drivers of other vehicles from communication modules of the other vehicles, generating tendency information of the drivers of the other vehicles on the basis of the driving information, and performing lane change control using the tendency information.

Disclosed are an apparatus and a method for controlling a lane change using V2V communication information and an apparatus for calculating tendency information for the same. According to the apparatuses and the method, it is possible to improve safety when changing lanes by receiving diving information of drivers of other vehicles from communication modules of the other vehicles, generating tendency information of the drivers of the other vehicles on the basis of the driving information, and performing lane change control using the tendency information.

The present invention relates to a device and a control method therefor and, more specifically, the device comprises: a memory for storing at least one command; a depth camera for capturing at least one hand of a user; a display module; and a controller for controlling the memory, the depth camera, and the display module. The controller controls the depth camera so as to capture the at least one hand of a user and controls the display module so as to output a visual feedback that changes on the basis of the captured hand of a user.

The present invention relates to a device and a control method therefor and, more specifically, the device comprises: a memory for storing at least one command; a depth camera for capturing at least one hand of a user; a display module; and a controller for controlling the memory, the depth camera, and the display module. The controller controls the depth camera so as to capture the at least one hand of a user and controls the display module so as to output a visual feedback that changes on the basis of the captured hand of a user.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

System, methods, and other embodiments described herein relate to training a depth model for joint depth completion and prediction. In one arrangement, a method includes generating depth features from sparse depth data according to a sparse auxiliary network (SAN) of a depth model. The method includes generating a first depth map from a monocular image and a second depth map from the monocular image and the depth features using the depth model. The method includes generating a depth loss from the second depth map and the sparse depth data and an image loss from the first depth map and the sparse depth data. The method includes updating the depth model including the SAN using the depth loss and the image loss.

A system receives a first set of points corresponding to an anatomical feature. Each point in the first set of points represents a position in a first frame. The system receives a second set of points corresponding to the anatomical feature. Each point in the second set of points represents a position in a second frame. The system identifies a first subset of the first set of points and determines a first transformation to align the first subset of the first set of points with the second set of points. The first set of points is transformed based on the first transformation. The system identifies a second subset of the first set of points and determines a second transformation to align the first and second subsets of the first set of points with the second set of points. The first set of points are transformed based on the second transformation.