Computer graphics applications controlled through natural gestures are gaining increasing popularity these days due to recent developments in low-cost tracking systems and gesture recognition technologies. Although interaction techniques through natural gestures have already demonstrated their benefits in manipulation, navigation and avatar-control tasks, effective selection with pointing gestures remains an open problem. In this paper we survey the state-of-the-art in 3D object selection techniques. We review important findings in human control models, analyze major factors influencing selection performance, and classify existing techniques according to a number of criteria. Unlike other components of the application's user interface, pointing techniques need a close coupling with the rendering pipeline, introducing new elements to be drawn, and potentially modifying the object layout and the way the scene is rendered. Conversely, selection performance is affected by rendering issues such as visual feedback, depth perception, and occlusion management. We thus review existing literature paying special attention to those aspects in the boundary between computer graphics and human computer interaction.

Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are low‐resolution except for selected places on Earth. In this paper we present a new method to turn low‐resolution DEMs into plausible and faithful high‐resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi‐resolution dictionaries), we benefit from high‐resolution aerial images to produce highly‐detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high‐resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.

The exploration of complex walkthrough models is often a difficult task due to the presence of densely occluded regions which pose a serious challenge to online navigation. In this paper we address the problem of algorithmic generation of exploration paths for complex walkthrough models. We present a characterization of suitable properties for camera paths and we discuss an efficient algorithm for computing them with little or no user intervention. Our approach is based on identifying the free-space structure of the scene (represented by a cell and portal graph) and an entropy-based measure of the relevance of a view-point. This metric is key for deciding which cells have to be visited and for computing critical way-points inside each cell. Several results on different model categories are presented and discussed.

This paper presents a new approach for generating coarse-level approximations of topologically complex models. Dramatic\ud topology reduction is achieved by converting a 3D model to and from a volumetric representation. Our approach produces valid,\ud error-bounded models and supports the creation of approximations that do not interpenetrate the original model, either being completely contained in the input solid or bounding it. Several simple to implement versions of our approach are presented and discussed. We show that these methods perform significantly better than other surface-based approaches when simplifying topologically-rich models such as scene parts and complex mechanical assemblies.Postprint (published version

We present an efficient method for generating coherent multi-layer landscapes. We use a dictionary built from exemplars to synthesize high-resolution fully-featured terrains from input low-resolution elevation data. Our example-based method consists in analyzing real world terrain examples and learning the procedural rules directly from these inputs. We take into account not only the elevation of the terrain, but also additional layers such as the slope, orientation, drainage area, the density and distribution of vegetation, and the soil type. By increasing the variety of terrain exemplars, our method allows the user to synthesize and control different types of landscapes and biomes, such as temperate or rain forests, arid deserts and mountains.