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A spatio-temporal regression architecture combining 3D CNNs and attention mechanisms that regresses these fused representations into a relative position estimate and demonstrates the effectiveness of the method on the TII Drone Racing and UZH-FPV datasets.
This work systematically evaluates the proposed approach on aerial RGB images from a public dataset as well as on noisy depth images captured onboard a drone using a lightweight depth camera, showing that the ViT-based model consistently outperforms widely used CNN architectures, including ResNet, particularly in low-d...
This work proposes a methodology using a binary network with a Continual Learning (CL) strategy to create an estimation model to create an estimation model during the same flight mission for Pose estimation using aerial images captured by UAVs.
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