Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.

Abstract: Systems, apparatuses and methods may provide for detecting a snapshot request to conduct a long range depth capture, wherein the snapshot request is associated with a short range depth capture. Additionally, an infrared (IR) projector may be activated at a first power level for a first duration in response to the snapshot request, wherein the first power level is greater than a second power level corresponding to the short range depth capture and the first duration is less than a second duration corresponding to the short range depth capture.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: Techniques are provided for 3D analysis of a scene including detection, segmentation and registration of objects within the scene. The analysis results may be used to implement augmented reality operations including removal and insertion of objects and the generation of blueprints. An example method may include receiving 3D image frames of the scene, each frame associated with a pose of a depth camera, and creating a 3D reconstruction of the scene based on depth pixels that are projected and accumulated into a global coordinate system. The method may also include detecting objects, and associated locations within the scene, based on the 3D reconstruction, the camera pose and the image frames. The method may further include segmenting the detected objects into points of the 3D reconstruction corresponding to contours of the object and registering the segmented objects to 3D models of the objects to determine their alignment.

Abstract: Techniques are provided for 3D analysis of a scene including detection, segmentation and registration of objects within the scene. The analysis results may be used to implement augmented reality operations including removal and insertion of objects and the generation of blueprints. An example method may include receiving 3D image frames of the scene, each frame associated with a pose of a depth camera, and creating a 3D reconstruction of the scene based on depth pixels that are projected and accumulated into a global coordinate system. The method may also include detecting objects, and associated locations within the scene, based on the 3D reconstruction, the camera pose and the image frames. The method may further include segmenting the detected objects into points of the 3D reconstruction corresponding to contours of the object and registering the segmented objects to 3D models of the objects to determine their alignment.

Abstract: Systems, apparatuses and methods may track air gestures within a bounding box in a field of view (FOV) of a camera. Air gestures made within the bounding box may be translated and mapped to a display screen. Should the hand, or other member making the air gesture, exit the bounds of the bounding box, the box will be dragged along by the member to a new location within the FOV.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: A head-mounted display (HMD) device to be worn by a user in a physical environment (PE) is controlled. A 3D virtual environment (VE) is modeled to include a virtual controllable object subject to virtual control input. Motion of the position, head, and hands of the user is monitored in the PE, and a physical surface in the PE is detected. A virtual user interface (vUI) is placed in the VE relative to a virtual perspective of the user. The vUI includes an information display and at least one virtual touch control to produce the virtual control input in response to virtual manipulation of the virtual touch control. The vUI's placement is determined to coincide with the physical surface in the PE relative to the position of the user in the PE.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: Systems and methods for combining three-dimensional tracking of a user's movements with a three-dimensional user interface display is described. A tracking module processes depth data of a user performing movements, for example, movements of the user's hands and fingers. The tracked movements are used to animate a representation of the hand and fingers, and the animated representation is displayed to the user using three-dimensional display. Also displayed are one or more virtual objects with which the user can interact. In some embodiments, the interaction of the user with the virtual objects controls an electronic device.

Abstract: Systems, apparatuses and methods may provide for detecting a snapshot request to conduct a long range depth capture, wherein the snapshot request is associated with a short range depth capture. Additionally, an infrared (IR) projector may be activated at a first power level for a first duration in response to the snapshot request, wherein the first power level is greater than a second power level corresponding to the short range depth capture and the first duration is less than a second duration corresponding to the short range depth capture.

Abstract: Various systems and methods for real-time cascaded object recognition are described herein. A system for real-time cascaded object recognition comprises a processor; and a memory, including instructions, which when executed on the processor, cause the processor to perform the operations comprising: accessing image data at the system, the image data of an environment around the system, the image data is captured by a camera system; determining a set of regions in the image data, the set of regions including candidate objects; transmitting a subset of the image data corresponding to the set of regions to a remote server, the remote server to analyze the subset of the image data and detect an object in the subset of the image data; and receiving at the system from the remote server, an indication of the object detected in the subset of the image data.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: Techniques are provided for 3D analysis of a scene including detection, segmentation and registration of objects within the scene. The analysis results may be used to implement augmented reality operations including removal and insertion of objects and the generation of blueprints. An example method may include receiving 3D image frames of the scene, each frame associated with a pose of a depth camera, and creating a 3D reconstruction of the scene based on depth pixels that are projected and accumulated into a global coordinate system. The method may also include detecting objects, and associated locations within the scene, based on the 3D reconstruction, the camera pose and the image frames. The method may further include segmenting the detected objects into points of the 3D reconstruction corresponding to contours of the object and registering the segmented objects to 3D models of the objects to determine their alignment.