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arXiv Computer Vision By Andr\'e Amorim, Pedro F. Proen\c{c}a

AnalogDepth: Multi-view Geometry from FPV drones under Analog Video Transmission

Read the original on arXiv Computer Vision →

AnalogDepth adapts the Depth Anything 3 (DA3) visual geometry model to the spatially structured noise of analog video transmission (VTX) used in FPV drones. The method employs a parameter‑efficient training pipeline that uses student‑teacher knowledge distillation with Low‑Rank Adaptation (LoRA) on the DINOv2 backbone, and trains on a noise bank built from real FPV recordings rather than synthetic Gaussian noise. Experiments on six real FPV flight sequences across three indoor scenes show that this real‑noise training consistently lowers per‑frame depth RMSE and 3D reconstruction Chamfer distance compared to the pretrained DA3 baseline and Gaussian noise baselines.

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