Abstract: Examples in the present disclosure relate to systems and methods for reducing noise in object tracking data. Images of an object are obtained via one or more cameras. The images are processed to obtain first pose data indicative of a pose of the object over time. The first pose data is represented in a camera space. The first pose data is transformed to second pose data represented in a world space. The second pose data is filtered using a smoothing filter to generate filtered pose data. The filtering includes, for each pose data item in a time series of the second pose data, using a rotation transformation between the world space and camera space to apply one or more camera space-specific filter parameters to the pose data item that is represented in the world space. The pose of the object is dynamically tracked based on the filtered pose data.

Abstract: An XR system that applies geometric constraints to a hand model is provided. The XR system captures tracking data using sensors and generates a hand model with joints based on the data. The XR system transforms joint positions into a normalized coordinate system and applies constraints to the hand model generate anatomically correct hand models. The XR uses the hand models to generate a user interface and displays the user interface to the user.

Abstract: Examples in the present disclosure relate to temporally sparse scale estimation for object tracking. A computing device detects a current orientation of an object. The computing device determines that a difference between the current orientation and at least one of a plurality of previously detected orientations of the object is less than a threshold value. Each previously detected orientation has a respective scale estimate. In response to determining that the difference is less than the threshold value, the computing device generates an effective scale estimate for the object based on a combination of the respective scale estimates for the plurality of previously detected orientations. Each respective scale estimate contributes to the effective scale estimate according to a respective difference between the current orientation and the previously detected orientation for the respective scale estimate. The computing device tracks a pose of the object based on the effective scale estimate.

Abstract: Examples in the present disclosure relate to systems and methods for reducing noise in object tracking data. Images of an object are obtained via one or more cameras. The images are processed to obtain first pose data indicative of a pose of the object over time. The first pose data is represented in a camera space. The first pose data is transformed to second pose data represented in a world space. The second pose data is filtered using a smoothing filter to generate filtered pose data. The filtering includes, for each pose data item in a time series of the second pose data, using a rotation transformation between the world space and camera space to apply one or more camera space-specific filter parameters to the pose data item that is represented in the world space. The pose of the object is dynamically tracked based on the filtered pose data.

Abstract: An example system for lightweight view dependent rendering is described herein. An example mobile device includes a display, instructions, and processor circuitry to execute instructions to extract a moving region within a first set of frames of volumetric video content captured by a plurality of real-world cameras facing a scene at different angles. The processor circuitry to generate a first billboard based on a cropped area of the frames in the first set of frames, the cropped area corresponding to the first moving region. The processor circuitry to, in response to user selection of a first view of the scene, cause presentation of the first billboard on the display. The processor circuitry to, in response to a change from the first view to a second view of the scene, cause a second billboard to replace the first billboard on the display.

Abstract: An example system for lightweight view dependent rendering is described herein. An example mobile device includes a display, instructions, and processor circuitry to execute instructions to extract a moving region within a first set of frames of volumetric video content captured by a plurality of real-world cameras facing a scene at different angles. The processor circuitry to generate a first billboard based on a cropped area of the frames in the first set of frames, the cropped area corresponding to the first moving region. The processor circuitry to, in response to user selection of a first view of the scene, cause presentation of the first billboard on the display. The processor circuitry to, in response to a change from the first view to a second view of the scene, cause a second billboard to replace the first billboard on the display.

Abstract: An example system for lightweight view dependent rendering is described herein. The system includes at least one memory, and at least one processor to execute instructions to track a moving object within a first view of a scene, the first view captured by a first camera of a plurality of cameras. The plurality of cameras to capture video data of the scene from a plurality of angles. The at least one processor to extract a portion of the video data to obtain a cropped video corresponding to the moving object within the first view. The at least one processor to generate a billboard representation based on the cropped video. The at least one processor to cause the billboard representation to be presented on a display in front of a two-dimensional background.

Abstract: An apparatus to facilitate encoding visibility data for view-dependent video is disclosed. The apparatus includes one or more processors to receive point cloud data including a plurality of images and camera parameters associated with each of a plurality of cameras, and encode one or more visibility descriptors for each point in the point cloud data to provide visibility information for each point for a plurality of camera views indicated in the camera parameters.

Abstract: An example system for lightweight view dependent rendering is described herein. The system includes at least one memory, and at least one processor to execute instructions to track a moving object within a first view of a scene, the first view captured by a first camera of a plurality of cameras. The plurality of cameras to capture video data of the scene from a plurality of angles. The at least one processor to extract a portion of the video data to obtain a cropped video corresponding to the moving object within the first view. The at least one processor to generate a billboard representation based on the cropped video. The at least one processor to cause the billboard representation to be presented on a display in front of a two-dimensional background.

Abstract: A system, article, and method of 3D reconstruction with volume-based filtering for image processing.

Abstract: An apparatus to facilitate encoding visibility data for view-dependent video is disclosed. The apparatus includes one or more processors to receive point cloud data including a plurality of images and camera parameters associated with each of a plurality of cameras, and encode one or more visibility descriptors for each point in the point cloud data to provide visibility information for each point for a plurality of camera views indicated in the camera parameters.

Abstract: An example system for lightweight view dependent rendering is described herein. The system includes a processor configured to determine a moving region of each camera view, wherein the moving region is defined by a bounding box and track each moving region to obtain a plurality of cropped videos for each camera view. The system may also be configured to generate a billboard for each camera view and render each billboard.

Abstract: A system, article, and method of 3D reconstruction with volume-based filtering for image processing.