Abstract: An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Abstract: An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Abstract: A binary depth mask model is trained for use in occlusion within a mixed reality application. The training leverages information about virtual object positions and depths that is inherently available to the system as part of rendering of virtual objects. Training is performed on a set of stereo images, a set of binary depth masks corresponding to the stereo images, and a depth value against which object depth is evaluated. Given this input, the training outputs the binary depth mask model, which when given a stereo image as input outputs a depth binary depth mask indicating which pixels of the stereo image are nearer, or father away, than the depth value. The depth mask model can be applied in real time to handle occlusion operations when compositing a given real-world stereo image with virtual objects.