  Recent advances in 3D scanning technology have enabled the deployment of 3D
models in various industrial applications like digital twins, remote inspection
and reverse engineering. Despite their evolving performance, 3D scanners, still
introduce noise and artifacts in the acquired dense models. In this work, we
propose a fast and robust denoising method for dense 3D scanned industrial
models. The proposed approach employs conditional variational autoencoders to
effectively filter face normals. Training and inference are performed in a
sliding patch setup reducing the size of the required training data and
execution times. We conducted extensive evaluation studies using 3D scanned and
CAD models. The results verify plausible denoising outcomes, demonstrating
similar or higher reconstruction accuracy, compared to other state-of-the-art
approaches. Specifically, for 3D models with more than 1e4 faces, the presented
pipeline is twice as fast as methods with equivalent reconstruction error.
