  Depth information is useful for many applications. Active depth sensors are
appealing because they obtain dense and accurate depth maps. However, due to
issues that range from power constraints to multi-sensor interference, these
sensors cannot always be continuously used. To overcome this limitation, we
propose an algorithm that estimates depth maps using concurrently collected
images and a previously measured depth map for dynamic scenes, where both the
camera and objects in the scene may be independently moving. To estimate depth
in these scenarios, our algorithm models the dynamic scene motion using
independent and rigid motions. It then uses the previous depth map to
efficiently estimate these rigid motions and obtain a new depth map. Our goal
is to balance the acquisition of depth between the active depth sensor and
computation, without incurring a large computational cost. Thus, we leverage
the prior depth information to avoid computationally expensive operations like
dense optical flow estimation or segmentation used in similar approaches. Our
approach can obtain dense depth maps at up to real-time (30 FPS) on a standard
laptop computer, which is orders of magnitude faster than similar approaches.
When evaluated using RGB-D datasets of various dynamic scenes, our approach
estimates depth maps with a mean relative error of 2.5% while reducing the
active depth sensor usage by over 90%.
