In this paper, we propose a mesh-based feature detection scheme that focuses on surface features. A class of features of key interest is intentional structures that act as fiducials and that, for instance, can assist in shape retrieval and distortion measurement. We introduce a tunable two-scale depth measurement scheme to quantify the displacement of a vertex from the local surface, which can be a strong indicator of features. We print and scan 3D models with fiducial features appearing across the surface to demonstrate the high fidelity and accuracy of the proposed feature detection scheme. The method outperforms existing 3D feature detection schemes on CAD models and 3D scans alike. We also discuss applications of data embedding enabled by the achievable detection performance.
This paper presents a new method for no reference mesh visual quality assessment using a convolutional neural network. To do this, we first render 2D images from multiple views of the 3D mesh. Then, each image is split into small patches which are learned to a convolutional neural network. The network consists of two convolutional layers with two max-pooling layers. Then, a multilayer perceptron (MLP) with two fully connected layers is integrated to summarize the learned representation into an output node. With this network structure, feature learning and regression are used to predict the quality score of a given distorted mesh without the availability of the reference mesh. Experiments have been successfully conducted on LIRIS/EPFL generalpurpose database. The obtained results show that the proposed method provides good correlation and competitive scores comparing to some influential and effective full and reduced reference methods.