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

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: An active rangefinder system disclosed herein parameterizes a set of transformations predicting different possible appearances of a projection feature projected into a three-dimensional scene. A matching module matches an image of the projected projection feature with one of the transformations, and a depth estimation module estimates a distance to an object reflecting the projection feature based on the transformation identified by the matching module.

Abstract: The subject disclosure is directed towards using a known projection pattern to make stereo (or other camera-based) depth detection more robust. Dots are detected in captured images and compared to the known projection pattern at different depths, to determine a matching confidence score at each depth. The confidence scores may be used as a basis for determining a depth at each dot location, which may be at sub-pixel resolution. The confidence scores also may be used as a basis for weights or the like for interpolating pixel depths to find depth values for pixels in between the pixels that correspond to the dot locations.