Saliency Mapping for Processing 3D Meshes in Industrial Modeling Applications The latest advancements in 3D scanning technologies have facilitated the generation and adoption of 3D models in several industrial applications ranging from manufacturing inspection and repair to digital twins and medical industry. All these applications require an accurate representation of physical objects through saliency mechanisms identifying certain areas of the 3D model that are considered as important information by humans. Hence, 3D saliency mapping is an essential mechanism in a number of 3D processing applications including denoising, compression, simplification, registration, viewpoint selection, etc. In this work, we propose a robust 3D saliency mapping method ideally suited for industrial 3D models with sharp and small scale geometric features. An extensive simulation study using a variety of 3D scanned and CAD models, verify the effectiveness of the proposed method as compared to other relevant approaches.

Fast mesh denoising with data driven normal filtering using deep autoencoders Through the years, several works have demonstrated highquality 3D mesh denoising. Despite the high reconstruction quality, there are still challenges that need to be addressed ranging from variations in configuration parameters to high computational complexity. These drawbacks are crucial especially if the reconstructed models have to be used for quality check, inspection or repair in manufacturing environments where we have to deal with large objects resulting in very dense 3D meshes. Recently, deep learning techniques have shown that are able to automatically learn and find more accurate and reliable results, without the need for setting manually parameters. In this work, motivated by the aforementioned requirements, we propose a fast and reliable denoising method that can be effectively applied for reconstructing very dense noisy 3D models. The proposed method applies conditional variational autoencoders on face normals. Extensive evaluation studies carried out using a variety of 3D models verify that the proposed approach achieves plausible reconstruction outputs, very relative or even better of those proposed by the literature, in considerably faster execution times.