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We present a 3d-laser-scan simulation in virtual
reality for creating synthetic scans of CAD models. Consisting of
the virtual reality head-mounted display Oculus Rift and the
motion controller Razer Hydra our system can be used like
common hand-held 3d laser scanners. It supports scanning of
triangular meshes as well as b-spline tensor product surfaces
based on high performance ray-casting algorithms. While point
clouds of known scanning simulations are missing the man-made
structure, our approach overcomes this problem by imitating
real scanning scenarios. Calculation speed, interactivity and the
resulting realistic point clouds are the benefits of this system.
Optical surface inspection: A novelty detection approach based on CNN-encoded texture features
(2018)
In inspection systems for textured surfaces, a reference texture is typically known before novel examples are inspected. Mostly, the reference is only available in a digital format. As a consequence, there is no dataset of defective examples available that could be used to train a classifier. We propose a texture model approach to novelty detection. The texture model uses features encoded by a convolutional neural network (CNN) trained on natural image data. The CNN activations represent the specific characteristics of the digital reference texture which are learned by a one-class classifier. We evaluate our novelty detector in a digital print inspection scenario. The inspection unit is based on a camera array and a flashing light illumination which allows for inline capturing of multichannel images at a high rate. In order to compare our results to manual inspection, we integrated our inspection unit into an industrial single-pass printing system.
Pascal Laube presents machine learning approaches for three key problems of reverse engineering of defective structured surfaces: parametrization of curves and surfaces, geometric primitive classification and inpainting of high-resolution textures. The proposed methods aim to improve the reconstruction quality while further automating the process. The contributions demonstrate that machine learning can be a viable part of the CAD reverse engineering pipeline.
Creating cages that enclose a 3D-model of some sort is part of many preprocessing pipelines in computational geometry. Creating a cage of preferably lower resolution than the original model is of special interest when performing an operation on the original model might be to costly. The desired operation can be applied to the cage first and then transferred to the enclosed model. With this paper the authors present a short survey of recent and well known methods for cage computation.
The authors would like to give the reader an insight in common methods and their differences.