EpiTransfer: 時系列単眼空中画像からのスパースで学習不要な長距離深度推定
EpiTransfer: Sparse, Training-Free Long-Range Depth Estimation from Temporal Monocular Aerial Frames
2枚の単眼画像とカメラ姿勢からエピポーラ転送により仮想ステレオペアを合成し、学習なしで長距離深度を推定する手法を提案。ドローン屋外・屋内でLiDARと比較し、学習ベース手法を上回る精度を示した。
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著者: Diksha Aggarwal, Rutvik Dagadkhair, Sanjana Srivastava, Bradley Denby, Kevin Kochersberger
分類: cs.RO, cs.CV
原文アブストラクト
Reliable 3D spatial understanding is essential for autonomous navigation, obstacle avoidance, and scene reconstruction. While state-of-the-art learned depth estimation techniques achieve high accuracy in-distribution, they often generalize poorly to novel viewpoints and altitudes. This paper presents a geometrically derived, training-free depth estimation method using epipolar transfer with only two monocular images and camera pose estimates. By leveraging camera motion to synthesize a virtual stereo pair with a freely chosen baseline, our approach transforms temporal correspondence into a stereo triangulation task while mitigating geometric degeneracies inherent to direct two-view triangulation. Validated across outdoor drone flights (to a maximum range of approximately 90\,m) and indoor OptiTrack environments against LiDAR ground truth, the method achieves an indoor AbsRel of 0.092 and $δ< 1.25$ of 0.940, comparable to direct triangulation (AbsRel 0.073) while retaining valid depth over a larger fraction of challenging scenes, and substantially outperforms off-the-shelf learning-based baselines such as ZoeDepth (AbsRel 0.225) and Depth Anything V2 (AbsRel 0.570), which are not trained or fine-tuned for this domain, with no training data required.
