Photometric bundle adjustment (PBA) accurately estimates geometry from video. However, current PBA systems have a temporary map that cannot manage scene reobservations. We present, direct sparse mapping, a full monocular visual simultaneous localization and mapping (SLAM) based on PBA. Its persistent map handles reobservations, yielding the most accurate results up to date on EuRoC for a direct method.

Mass-Spring Models (MSMs) are used to simulate the mechanical behavior of deformable bodies such as soft tissues in medical applications. Although they are fast to compute, they lack accuracy and their design remains still a great challenge. The major difficulties in building realistic MSMs lie on the spring stiffness estimation and the topology identification. In this work, the mechanical behavior of MSMs under tensile loads is analyzed before studying the spring stiffness estimation. In particular, the performed qualitative and quantitative analysis of the behavior of cubical MSMs shows that they have a nonlinear response similar to hyperelastic material models. According to this behavior, a new method for spring stiffness estimation valid for linear and nonlinear material models is proposed. This method adjusts the stress-strain and compressibility curves to a given reference behavior. The accuracy of the MSMs designed with this method is tested taking as reference some soft-tissue simulations based on nonlinear Finite Element Method (FEM). The obtained results show that MSMs can be designed to realistically model the behavior of hyperelastic materials such as soft tissues and can become an interesting alternative to other approaches such as nonlinear FEM.

Virtual reality (VR)- and augmented reality (AR)-based simulations are key technologies in Industry 4.0 which allow for testing and studying of new processes before their deployment. A simulator of industrial processes needs a flexible way in which to model the activities performed by the worker and other elements involved, such as robots and machinery. This work proposes a framework to model industrial processes for VR and AR simulators. The desk method was used to review previous research and extract the most important features of current approaches. Novel features include interaction among human workers and a variety of automation systems, such as collaborative robots, a broader set of tasks (including assembly and disassembly of components), flexibility of modeling industrial processes for different domains and purposes, a clear separation of process definition and simulator, and independence of specific programming languages or technologies. Three industrial scenarios modeled with this framework are presented: an aircraft assembly scenario, a guidance tool for high-voltage cell security, and an application for the training of machine-tool usage.

A proposed real-time neurosurgery simulator handles skull drilling and surgical interaction with the brain. This involves the development and combination of areas such as collision handling, haptic rendering, physical simulation, and volumetric visualization. The simulator's input data comes from computed-tomography and magnetic-resonance-imaging images of the patients. Collision detection for drilling uses only density data; collision detection for interaction with the brain is based on uniform spatial subdivision of a tetrahedral mesh. To take advantage of all the information, the simulator employs visualization methods such as volumetric isosurfaces and deformable volume rendering.