Denoising of Time-of-Flight depth data via iteratively reweighted least squares minimization

Time-of-Flight depth data suffer from spatially varying noise, whose variance is inversely proportional to the squared amplitude of the received signal. On the other hand, preservation of genuine discontinuities of the scene is an important quality for a denoising method to have. This paper presents a noise-aware and discontinuity-preserving Time-of-Flight depth de-noising method. To incorporate different constraints from the two philosophies, we recast depth denoising into an iteratively reweighted least squares problem, in which the cost function is iteratively updated and minimized in a manner of preserving the discontinuities and rejecting outliers while denoising the depth data. The experiments show that the proposed method delivers better results with lower error than existing methods, irrespective of the amount of noise and discontinuities.