Kernel Density Filtering for Noisy Point Clouds in One Step

We present a method for filtering noisy point clouds, specifically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have previously been used in finding outliers in clustered data, but not in MVS or range scanning. We estimate the probability density function (PDF) over the space of observed points via a technique called kernel density estimation. We utilize Mahalanobis distance and a variable bandwidth for weighting kernels accordingly, based on the nature of neighbouring points. Further, we incorporate a distance metric called the Reachability Distance that, as we show in our results, gives better discrimination than a classical Mahalanobis distancebased metric. With the addition of this nearest neighbour metric, we can produce results that are ready for meshing without any post-processing of the cloud. We mesh our filtered point clouds using a traditional surface fitting technique that is unequipped to deal with noise to demonstrate the efficacy of our method.