Abstract: A system configured to determine poses of a pair hand-held controller in a physical environment and to utilize the poses as an input to control or manipulate a virtual environment or mixed reality environment. In some cases, the system may capture image data of the controllers having constellations or patterns. The system may analyze the image data to identify points associated with the constellations or patterns and to determine the poses and disambiguate the identity of the individual controllers based on the identified points and a stored model associated with each controller.

Abstract: A system configured to improve the operations associated with generating virtual representations on limited resources of a mobile device. In some cases, the system may utilize viewpoint bundles that include collection of image data with an associated pose in relative physical proximity to each other to render a virtual scene. In other cases, the system may utilize 2.5D manifolds including 2D image data and a weighted depth value to render the 3D environment.

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 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 system configured to improve the operations associated with generating virtual representations on limited resources of a mobile device. In some cases, the system may utilize viewpoint bundles that include collection of image data with an associated pose in relative physical proximity to each other to render a virtual scene. In other cases, the system may utilize 2.5D manifolds including 2D image data and a weighted depth value to render the 3D environment.

Abstract: A system configured to improve the operations associated with generating virtual representations on limited resources of a mobile device. In some cases, the system may utilize viewpoint bundles that include collection of image data with an associated pose in relative physical proximity to each other to render a virtual scene. In other cases, the system may utilize 2.5D manifolds including 2D image data and a weighted depth value to render the 3D environment.

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 system configured to improve the operations associated with generating virtual representations of physical environments to recognize the physical environments and/or relocalize within the virtual representations in a substantially real time system. In some cases, the system may use a first pre-training phase of descriptors and/or split nodes of regression forests using features common across various scenes to learn general image appearance, and a second training phase of descriptors and/or leaf nodes of regression forests to learn scene specific features. The system may align the features using an orientation vector, correct for camera perspective and lens distortion of the features as well as learn robust illumination invariant features from real and synthetic data.

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 system configured to improve the operations associated with generating virtual representations of physical environments to recognize the physical environments and/or relocalize within the virtual representations in a substantially real time system. In some cases, the system may use a first pre-training phase of descriptors and/or split nodes of regression forests using features common across various scenes to learn general image appearance, and a second training phase of descriptors and/or leaf nodes of regression forests to learn scene specific features. The system may align the features using an orientation vector, correct for camera perspective and lens distortion of the features as well as learn robust illumination invariant features from real and synthetic data.

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 system configured to improve the operations associated with generating virtual representations of physical environments to recognize the physical environments and/or relocalize within the virtual representations in a substantially real time system. In some cases, the system may use a first pre-training phase of descriptors and/or split nodes of regression forests using features common across various scenes to learn general image appearance, and a second training phase of descriptors and/or leaf nodes of regression forests to learn scene specific features. The system may align the features using an orientation vector, correct for camera perspective and lens distortion of the features as well as learn robust illumination invariant features from real and synthetic data.

Abstract: A system configured to improve the operations associated with generating virtual representations on limited resources of a mobile device. In some cases, the system may utilize viewpoint bundles that include collection of image data with an associated pose in relative physical proximity to each other to render a virtual scene. In other cases, the system may utilize 2.5D manifolds including 2D image data and a weighted depth value to render the 3D environment.

Abstract: A system configured to improve the operations associated with generating virtual representations on limited resources of a mobile device. In some cases, the system may utilize viewpoint bundles that include collection of image data with an associated pose in relative physical proximity to each other to render a virtual scene. In other cases, the system may utilize 2.5D manifolds including 2D image data and a weighted depth value to render the 3D environment.

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 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: Certain exemplary embodiments comprise a method, which can comprise automatically causing a representation of body tissue to be rendered. The body tissue can be tracked via clusters. The clusters each can comprise a predetermined number of particles. Each particle of a particular cluster can be representative of a discreet path associated with the particular cluster in a tensor field.

Abstract: Certain exemplary embodiments comprise a method, which can comprise automatically causing a representation of body tissue to be rendered. The body tissue can be tracked via clusters. The clusters each can comprise a predetermined number of particles. Each particle of a particular cluster can be representative of a discreet path associated with the particular cluster in a tensor field.