An exemplary method includes maintaining a receiver-side mesh-vertices list, receiving duplicative-vertex information from a sender, and responsively reducing the receiver-side mesh-vertices list in accordance with the received duplicative-vertex information, and rendering, using the reduced receiver-side mesh-vertices list, viewpoint-adaptive three-dimensional (3D) personas of a subject at least in part by weighting video pixel colors from different video-camera vantage points of video cameras that capture video streams of the subject, the weighting being performed according to a respective geometric relationship of each video-camera vantage point to a user-selected viewpoint.

An exemplary method includes maintaining a receiver-side mesh-vertices list, receiving duplicative-vertex information from a sender, and responsively reducing the receiver-side mesh-vertices list in accordance with the received duplicative-vertex information, and rendering, using the reduced receiver-side mesh-vertices list, viewpoint-adaptive three-dimensional (3D) personas of a subject at least in part by weighting video pixel colors from different video-camera vantage points of video cameras that capture video streams of the subject, the weighting being performed according to a respective geometric relationship of each video-camera vantage point to a user-selected viewpoint.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets. Each tissue parameter set is assigned with signal evolution data pre-calculated for multiple SSFP states.

An object of the invention is to efficiently create a 3D model of a plant with attributes from a 3D model of a plant with no attributes. In order to solve the above problems, in the invention, a connection information conversion part 5 converts a connection relationship of parts extracted from a 3D model with no attributes 2 into connection information of a system diagram, an extraction information comparing part 6 compares the connection information with the connection relationship extracted from an attribute system diagram to create an conversion correspondence DB 7, and a 3D model with attributes 9 is created based on the conversion correspondence DB from the 3D model with no attributes 2.

A method to culling parts of a 3D reconstruction volume is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data with low usage of computational resources and storage spaces. The method includes culling parts of the 3D reconstruction volume against a depth image. The depth image has a plurality of pixels, each of which represents a distance to a surface in a scene. In some embodiments, the method includes culling parts of the 3D reconstruction volume against a frustum. The frustum is derived from a field of view of an image sensor, from which image data to create the 3D reconstruction is obtained.

Disclosed are a method and an apparatus for detecting abnormal objects in a video. The method for detecting abnormal objects in a video reconstructs a restored batch by applying each input batch to which an inpainting pattern is applied to a trained auto-encoder model, and fuses a time domain reconstruction error using time domain restored frames output by extracting and restoring a time domain feature point by applying a spatial domain reconstruction error and a plurality of successive frames using a restored frame output by combining the reconstructed restoring batch to a trained LSTM auto-encoder model to estimate an area where an abnormal object is positioned.

Disclosed are a method and an apparatus for detecting abnormal objects in a video. The method for detecting abnormal objects in a video reconstructs a restored batch by applying each input batch to which an inpainting pattern is applied to a trained auto-encoder model, and fuses a time domain reconstruction error using time domain restored frames output by extracting and restoring a time domain feature point by applying a spatial domain reconstruction error and a plurality of successive frames using a restored frame output by combining the reconstructed restoring batch to a trained LSTM auto-encoder model to estimate an area where an abnormal object is positioned.

It is intended to promote enhancement of performance of acquiring a depth image. A depth image acquiring apparatus includes a light emitting diode, a TOF sensor, and a filter. The light emitting diode irradiates modulated light toward a detection area becoming an area in which a depth image is to be acquired to detect a distance. The TOF sensor receives incident light into which the light irradiated from the light emitting diode is reflected by an object lying in the detection area to become, thereby outputting a signal used to produce the depth image. The filter passes more light having a wavelength in a predetermined pass bandwidth than light having a wavelength in a pass bandwidth other than the predetermined pass bandwidth of the light made incident toward the TOF sensor. In this case, at least one of the light emitting diode, the TOF sensor, or arrangement of the filter is controlled in accordance with a temperature of the light emitting diode or the TOF sensor. The present technique, for example, can be applied to a system for with international search report acquiring a depth image by using a TOF system.