Reciprocal Visibility
This addresses occlusion removal in drone-based synthetic aperture imaging, which is an incremental improvement for aerial photography and surveillance applications.
The paper tackles the problem of optimizing drone sampling positions for occlusion removal by introducing reciprocal visibility, which determines visibility of potential sampling positions from ground points of interest, and demonstrates a greedy sampling optimization approach.
We propose a guidance strategy to optimize real-time synthetic aperture sampling for occlusion removal with drones by pre-scanned point-cloud data. Depth information can be used to compute visibility of points on the ground for individual drone positions in the air. Inspired by Helmholtz reciprocity, we introduce reciprocal visibility to determine the dual situation - the visibility of potential sampling position in the air from given points of interest on the ground. The resulting visibility map encodes which point on the ground is visible by which magnitude from any position in the air. Based on such a map, we demonstrate a first greedy sampling optimization.