Optimizing Placement of Commodity Depth Cameras for known 3D Dynamic Scene Capture
This project presents a novel algorithmic framework and an off-line optimization of depth cameras placements for a given 3D dynamic scene, simulated using virtual 3D models. We derive a fitness metric for a particular configuration of sensors by combining factors such as visibility and resolution of the entire dynamic scene with probabilities of interference between sensors. We employ this fitness metric both in a greedy algorithm that determines the number of depth cameras needed to cover the scene, and in a simulated annealing algorithm that optimizes the placements of those sensors.
Accepted for IEEE VR, 2017
Optimizing Placement of Commodity Depth Cameras for known 3D Dynamic Scene Capture Rohan Chabra, Adrian Ilie, Nicholas Rewkowski, Young-Woon Cha and Henry Fuchs