Abstract: A three-dimensional (3D) scene is computationally reconstructed using a combination of plural modeling techniques. Point clouds representing an object in the 3D scene are generated by different modeled techniques and each point is encoded with a confidence value which reflects a degree of accuracy in describing the surface of the object in the 3D scene based on strengths and weaknesses of each modeling technique. The point clouds are merged in which a point for each location on the object is selected according to the modeling technique that provides the highest confidence.

Abstract: Various examples related to determining a location of an active speaker are provided. In one example, image data of a room from an image capture device is received and a three dimensional model is generated. First audio data from a first microphone array at the image capture device is received. Second audio data from a second microphone array laterally spaced from the image capture device is received. Using the three dimensional model, a location of the second microphone array with respect to the image capture device is determined. Using the audio data and the location and angular orientation of the second microphone array, an estimated location of the active speaker is determined. Using the estimated location, a setting for the image capture device is determined and outputted to highlight the active speaker.

Abstract: A three-dimensional (3D) scene is computationally reconstructed using a combination of plural modeling techniques. Point clouds representing an object in the 3D scene are generated by different modeled techniques and each point is encoded with a confidence value which reflects a degree of accuracy in describing the surface of the object in the 3D scene based on strengths and weaknesses of each modeling technique. The point clouds are merged in which a point for each location on the object is selected according to the modeling technique that provides the highest confidence.

Abstract: The description relates to depth images and obtaining higher resolution depth images through depth dependent measurement modeling. One example can receive a set of depth images of a scene captured by a depth camera. The example can obtain a depth dependent pixel averaging function for the depth camera. The example can also generate a high resolution depth image of the scene from the set of depth images utilizing the depth dependent pixel averaging function.

Abstract: An “Oriented Disk Interpolator” provides various techniques for interpolating between points in a point cloud using RGB images (or images in other color spaces) to produce a smooth implicit surface representation that can then be digitally sampled for ray-tracing or meshing to create a high fidelity geometric proxy from the point cloud. More specifically, the Oriented Disk Interpolator uses image color-based consistency to build an implicit surface from oriented points and images of the scene by interpolating disks in 3D space relative to a point cloud of a scene or objects within the scene. The resulting implicit surface is then available for a number of uses, including, but not limited to, constructing a high fidelity geometric proxy.

Abstract: A “Dynamic High Definition Bubble Framework” allows local clients to display and navigate FVV of complex multi-resolution and multi-viewpoint scenes while reducing computational overhead and bandwidth for rendering and/or transmitting the FVV. Generally, the FVV is presented to the user as a broad area from some distance away. Then, as the user zooms in or changes viewpoints, one or more areas of the overall area are provided in higher definition or fidelity. Therefore, rather than capturing and providing high definition everywhere (at high computational and bandwidth costs), the Dynamic High Definition Bubble Framework captures one or more “bubbles” or volumetric regions in higher definition in locations where it is believed that the user will be most interested. This information is then provided to the client to allow individual clients to navigate and zoom different regions of the FVV during playback without losing fidelity or resolution in the zoomed areas.

Abstract: An “Oriented Disk Interpolator” provides various techniques for interpolating between points in a point cloud using RGB images (or images in other color spaces) to produce a smooth implicit surface representation that can then be digitally sampled for ray-tracing or meshing to create a high fidelity geometric proxy from the point cloud. More specifically, the Oriented Disk Interpolator uses image color-based consistency to build an implicit surface from oriented points and images of the scene by interpolating disks in 3D space relative to a point cloud of a scene or objects within the scene. The resulting implicit surface is then available for a number of uses, including, but not limited to, constructing a high fidelity geometric proxy.

Abstract: Cloud based FVV streaming technique embodiments presented herein generally employ a cloud based FVV pipeline to create, render and transmit FVV frames depicting a captured scene as would be viewed from a current synthetic viewpoint selected by an end user and received from a client computing device. The FVV frames use a similar level of bandwidth as a conventional streaming movie would consume. To change viewpoints, a new viewpoint is sent from the client to the cloud, and a new streaming movie is initiated from the new viewpoint. Frames associated with that viewpoint are created, rendered and transmitted to the client until a new viewpoint request is received.

Abstract: Free viewpoint video of a scene is generated and presented to a user. An arrangement of sensors generates streams of sensor data each of which represents the scene from a different geometric perspective. The sensor data streams are calibrated. A scene proxy is generated from the calibrated sensor data streams. The scene proxy geometrically describes the scene as a function of time and includes one or more types of geometric proxy data which is matched to a first set of current pipeline conditions in order to maximize the photo-realism of the free viewpoint video resulting from the scene proxy at each point in time. A current synthetic viewpoint of the scene is generated from the scene proxy. This viewpoint generation maximizes the photo-realism of the current synthetic viewpoint based upon a second set of current pipeline conditions. The current synthetic viewpoint is displayed.