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About this paper
Daryl Peralta, Joel Casimiro, Aldrin Michael Nilles, Justine Aletta Aguilar, Rowel Atienza, Rhandley Cajote
Scan-RL trains a policy to choose drone-camera views from images for synthetic house reconstruction. The policy uses images at decision time; depth and ground-truth points inform training rewards. In one tested configuration, it reached 96% surface coverage in 15 steps and 533.34 distance units, versus 18 steps and 641.38 units for the best circular baseline. These simulated results do not establish real-drone performance.
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