Abstract: A virtual reality or mixed reality system configured to preform object detection using a monocular camera. The system configured to make the user aware of the detected objects by showing edges or lines of the object within a virtual scene. Thus, the user the user is able to avoid injury or collision while immersed in the virtual scene. In some cases, the system may also detect and correct for drift in the six degree of freedom pose of the user using corrections based on the current motion of the users.

Abstract: A method for automatic registration of 3D image data, captured by a 3D image capture system having an RGB camera and a depth camera, includes capturing 2D image data with the RGB camera at a first pose; capturing depth data with the depth camera at the first pose; performing an initial registration of the RGB camera to the depth camera; capturing 2D image data with the RGB camera at a second pose; capturing depth data at the second pose; and calculating an updated registration of the RGB camera to the depth camera.

Abstract: A head mounted display is provided that includes a main body, a sensor, and a door. The door of the head mounted display includes a computing device or, in some cases, an embedded screen. The computing device includes a camera and a display interface. The sensor and the camera of the mobile computing device are rigidly positioned with fixed displacements.

Abstract: A three-dimensional (3D) sensing apparatus together with a projector subassembly is provided. The 3D sensing apparatus includes two cameras, which may be configured to capture ultraviolet and/or near-infrared light. The 3D sensing apparatus may also contain an optical filter and one or more computing processors that signal a simultaneous capture using the two cameras and processing the captured images into depth. The projector subassembly of the 3D sensing apparatus includes a laser diode, one or optical elements, and a photodiode that are useable to enable 3D capture.

Abstract: A three-dimensional (3D) sensing apparatus together with a projector subassembly is provided. The 3D sensing apparatus includes two cameras, which may be configured to capture ultraviolet and/or near-infrared light. The 3D sensing apparatus may also contain an optical filter and one or more computing processors that signal a simultaneous capture using the two cameras and processing the captured images into depth. The projector subassembly of the 3D sensing apparatus includes a laser diode, one or optical elements, and a photodiode that are useable to enable 3D capture.

Abstract: In a system and method of estimating and classifying a barcode using heuristic and statistical measures, a classification determination is generated for each of a plurality of estimated barcode digits that correspond to a decoded barcode estimation. The classification determination is selected from one of a first classification determination and a second classification determination. The first classification determinations among a plurality of estimation determinations are aggregated. The aggregated first classification determinations are compared to a first predetermined threshold to determine a validity of the decoded barcode estimation.

Abstract: In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.

Abstract: In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.

Abstract: One variation of a real-time 3D capture system for a mobile electronic device having a camera includes an infrared projector that projects a pattern onto an imaging target; an infrared sensor that captures the pattern; a control module that controls the projector and sensor, takes data from the sensor, determines depth information from the data, and transmits the depth information to the mobile electronic device; a battery that provides power to the projector, sensor, and control module; a software module connected to the mobile electronic device that controls communication of data from the camera and depth information between the control module and the mobile electronic device; a mounting bracket that removably attaches the apparatus to the mobile electronic device such that the capture system when attached maintains alignment with the camera; and a chassis that holds the projector, sensor, control module, and battery, and attaches to the mounting bracket.

Abstract: In a system and method of estimating and classifying a barcode using heuristic and statistical measures, a classification determination is generated for each of a plurality of estimated barcode digits that correspond to a decoded barcode estimation. The classification determination is selected from one of a first classification determination and a second classification determination. The first classification determinations among a plurality of estimation determinations are aggregated. The aggregated first classification determinations are compared to a first predetermined threshold to determine a validity of the decoded barcode estimation.

Abstract: In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.

Abstract: In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.

Abstract: In a system and method of estimating and classifying a barcode using heuristic and statistical measures, a classification determination is generated for each of a plurality of estimated barcode digits that correspond to a decoded barcode estimation. The classification determination is selected from one of a first classification determination and a second classification determination. The first classification determinations among a plurality of estimation determinations are aggregated. The aggregated first classification determinations are compared to a first predetermined threshold to determine a validity of the decoded barcode estimation.

Abstract: In a system and method of estimating and classifying a barcode using heuristic and statistical measures, a classification determination is generated for each of a plurality of estimated barcode digits that correspond to a decoded barcode estimation. The classification determination is selected from one of a first classification determination and a second classification determination. The first classification determinations among a plurality of estimation determinations are aggregated. The aggregated first classification determinations are compared to a first predetermined threshold to determine a validity of the decoded barcode estimation.

Abstract: In a system and method of recognizing a barcode from a stream of video frames, a processor-implemented camera module receives a stream of video frames, with at least one video frame including a barcode. A processor-implemented barcode blur estimate module estimates an amount of defocus blur in a video frame. The processor-implemented barcode blur estimate module further estimates an identity of the barcode. A processor-implemented barcode localization module identifies a region of the video frame containing the barcode. A processor-implemented barcode geometric modeler module generates a geometric model of the barcode that includes an identified barcode deformity. A processor-implemented barcode decoder module decodes the barcode from the video frame using the estimated amount of defocus blur, the estimated identity of the barcode, and the geometric model of the barcode.