发表机构
NOVA School of Science and Technology, NOVA University Lisbon（新里斯本大学科技学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对模拟视频传输噪声严重损害FPV无人机深度估计的问题，提出AnalogDepth，利用噪声库和LoRA知识蒸馏适配DA3模型，实验证明能有效降低深度误差和重建误差。
AI 中文摘要
模拟视频传输（VTX）由于低延迟、轻重量和低成本，在FPV无人机中仍然广泛使用。然而，模拟VTX遭受复杂的空间结构化图像退化，这与标准训练增强中使用的数字图像损坏（例如AWGN）有根本不同。这项工作表明，这种类型的噪声严重降低了Depth Anything 3（DA3）的准确性，DA3是一种最先进的前馈视觉几何基础模型。为了解决这一差距，我们提出了AnalogDepth，一种参数高效的训练流程，通过学生-教师知识蒸馏和注入到DINOv2骨干中的低秩适应（LoRA）将DA3适应于模拟FPV图像。我们不是分析性地合成噪声，而是从不同条件下的静态FPV记录中构建噪声库，并将真实噪声注入与PSD匹配的高斯合成和AWGN作为基线进行比较。在三个室内场景的六个真实FPV飞行序列上的实验表明，与预训练的DA3基线和两种高斯噪声变体相比，使用我们的噪声库进行训练一致地降低了每帧深度RMSE和3D重建Chamfer距离。这些结果表明，复制真实模拟传输噪声的空间结构对于有效适应至关重要。
英文摘要
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.
