In this paper, we present an Augmented Reality (AR) game for finding and learning about endangered animals in a fun way. It uses tangible cubes as the user interface. This game was included in the activity program of the Summer School of the Universidad Politecnica de Valencia. Forty-six children played the AR game and the equivalent real game. We have compared the results of the two games. The results indicate that children enjoyed playing the AR game more than playing the real game and that they perceived the AR game to be more fun than the real game. They also preferred the AR game to the real one. The children perceived the real game as being easier to play than the AR game. The children also seemed to learn about the subject of endangered animals.

High-resolution 3D scanning devices produce high-density point clouds, which require a large capacity of storage and time-consuming processing algorithms. In order to reduce both needs, it is common to apply surface simplification algorithms as a preprocessing stage. The goal of point cloud simplification algorithms is to reduce the volume of data while preserving the most relevant features of the original point cloud. In this paper, we present a new point cloud feature-preserving simplification algorithm. We use a global approach to detect saliencies on a given point cloud. Our method estimates a feature vector for each point in the cloud. The components of the feature vector are the normal vector coordinates, the point coordinates, and the surface curvature at each point. Feature vectors are used as basis signals to carry out a dictionary learning process, producing a trained dictionary. We perform the corresponding sparse coding process to produce a sparse matrix. To detect the saliencies, the proposed method uses two measures, the first of which takes into account the quantity of nonzero elements in each column vector of the sparse matrix and the second the reconstruction error of each signal. These measures are then combined to produce the final saliency value for each point in the cloud. Next, we proceed with the simplification of the point cloud, guided by the detected saliency and using the saliency values of each point as a dynamic clusterization radius. We validate the proposed method by comparing it with a set of state-of-the-art methods, demonstrating the effectiveness of the simplification method.

This paper presents an Augmented Reality (AR) game for learning words. Thirty-two children played the AR game and the equivalent real game. We have compared the results of the two games. The results indicate that children did not found significant differences between the two games except for one question, but 81% of the children liked most the AR game.