Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Abstract

Considering that the existing depth recovery approaches have different limitations when applied to Kinect depth data, in this article, we propose to integrate their effective features including adaptive support region selection, reliable depth selection, and color guidance together under an optimization framework for Kinect depth recovery. In particular, we formulate our depth recovery as an energy minimization problem, which solves the depth hole filling and denoising simultaneously. The energy function consists of a fidelity term and a regularization term, which are designed according to the Kinect characteristics. Our framework inherits and improves the idea of guided filtering by incorporating structure information and prior knowledge of the Kinect noise model. Through analyzing the solution to the optimization framework, we also derive a local filtering version that provides an efficient and effective way of improving the existing filtering techniques. Quantitative evaluations on our developed synthesized dataset and experiments on real Kinect data show that the proposed method achieves superior performance in terms of recovery accuracy and visual quality.