Abstract
Multi-view stereo techniques with traditional cameras have wide applications in robotics and computer vision for scene reconstruction. Their dependence on the visible spectrum, however, poses several limitations that radar sensing could overcome in obstructing conditions such as fog and smoke. We propose a new radar-based multi-view stereo method for scene reconstruction, which combines the power of multi-view stereo techniques with the advantages of radar sensing by extending upon our previous work in this direction, where we demonstrated a time-domain inversion approach by leveraging a set of independent radar echoes acquired at sparse locations to reconstruct the scene's geometry. Here, we show how radar stretch processing can be incorporated into a similar geometric framework to leverage frequency-domain information. Our method fundamentally differs from classical radar imaging by utilizing an explicit geometric shape representation, allowing the imposition of shape priors and the ability to model visibility and occlusions, and a forward model based on the electric field strength density over the antenna range embedded within the deramped echo. An iterative scheme is then used to evolve an initial shape toward an optimal configuration to best explain the data. We conclude by showing the initial proof of concept for the success of this method through a set of simulated 2D experiments of increasing complexity.