  Autonomous mobile robots like self-flying drones and industrial robots
heavily depend on depth images to perform tasks such as 3D reconstruction and
visual SLAM. However, the presence of inaccuracies in these depth images can
greatly hinder the effectiveness of these applications, resulting in
sub-optimal results. Depth images produced by commercially available cameras
frequently exhibit noise, which manifests as flickering pixels and erroneous
patches. ML-based methods to rectify these images are unsuitable for edge
devices that have very limited computational resources. Non-ML methods are much
faster but have limited accuracy, especially for correcting errors that are a
result of occlusion and camera movement. We propose a scheme called VoxDepth
that is fast, accurate, and runs very well on edge devices. It relies on a host
of novel techniques: 3D point cloud construction and fusion, and using it to
create a template that can fix erroneous depth images. VoxDepth shows superior
results on both synthetic and real-world datasets. We demonstrate a 31%
improvement in quality as compared to state-of-the-art methods on real-world
depth datasets, while maintaining a competitive framerate of 27 FPS (frames per
second).
