VoxDepth: Rectification of Depth Images on Edge Devices

Abstract

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).