We present an algorithm for the restoration of noisy point cloud data, termed Moving Robust Principal Components Analysis (MRPCA). We model the point cloud as a collection of overlapping two‐dimensional subspaces, and propose a model that encourages collaboration between overlapping neighbourhoods. Similar to state‐of‐the‐art sparse modelling‐based image denoising, the estimated point positions are computed by local averaging. In addition, the proposed approach models grossly corrupted observations explicitly, does not require oriented normals, and takes into account both local and global structure. Sharp features are preserved via a weighted ℓ1 minimization, where the weights measure the similarity between normal vectors in a local neighbourhood. The proposed algorithm is compared against existing point cloud denoising methods, obtaining competitive results.

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We present a novel semi-supervised framework for training classifiers and simultaneously detecting out-of-distribution inputs. We do this by training on an closed classification dataset and an auxiliary simulated-open dataset, which consists of examples from outside the closed set. Through unsupervised learning and incorporating a class-distance value for each known class, we can identify out-ofdistribution RF devices with state-of-the-art accuracy. We define metrics for quantifying robustness in terms of both classification and Open Set Recognition (OSR). Finally, we discuss uncertainty estimation and calibrate our open set predictions so that they represent confidence. CCS CONCEPTS• Computing methodologies → Anomaly detection.

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