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

2026-09-21

AI Summary

本文针对FPV无人机模拟视频传输中的图像退化问题，提出AnalogDepth方法，通过学生-教师知识蒸馏和低秩适应技术改进深度估计准确性。

Abstract

Analog video transmission (VTX) remains widespread in FPV drones due to low latency, weight and low cost. However analog VTX suffers from complex spatially structured image degradation which differ fundamentally from digital image corruption (e.g. AWGN) used in standard training augmentation. This work shows that this type of noise severely degrades the accuracy of Depth Anything 3 (DA3), a state-of-the-art feed forward visual geometry foundation model. To address this gap, we present AnalogDepth, a parameter-efficient training pipeline that adapts DA3 to analog FPV imagery using student-teacher knowledge distillation with Low-Rank Adaptation (LoRA) injected into the DINOv2 backbone. Rather than synthesizing noise analytically, we build a noise bank from static FPV recordings under diverse conditions and compare real-noise injection against PSD-matched Gaussian synthesis and AWGN as baselines. Experiments on six real FPV flight sequences across three indoor scenes show that training with our noise bank consistently reduces per-frame depth RMSE and 3D reconstruction Chamfer distance compared to the pretrained DA3 baseline and both Gaussian noise variants. These results demonstrate that replicating the spatial structure of real analog transmission noise is critical for effective adaptation.

Problem

• Analog Video Transmission
• FPV Drones
• Spatially Structured Image Degradation
• Depth Accuracy

Innovation

• AnalogDepth
• Student-Teacher Knowledge Distillation
• Low-Rank Adaptation (LoRA)
• Real-Noise Bank
• DINOv2