Autonomous UAV forestry operations require robust depth estimation with strong cross-domain generalization, yet existing evaluations focus on urban and indoor scenarios, leaving a critical gap for vegetation-dense environments. We present the first systematic zero-shot evaluation of eight stereo methods spanning iterative refinement, foundation model, diffusion-based, and 3D CNN paradigms. All methods use officially released pretrained weights (trained on Scene Flow) and are evaluated on four standard benchmarks (ETH3D, KITTI 2012/2015, Middlebury) plus a novel 5,313-pair Canterbury Tree Branches dataset ($1920 \times 1080$). Results reveal scene-dependent patterns: foundation models excel on structured scenes (BridgeDepth: 0.23 px on ETH3D; DEFOM: 4.65 px on Middlebury), while iterative methods show variable cross-benchmark performance (IGEV++: 0.36 px on ETH3D but 6.77 px on Middlebury; IGEV: 0.33 px on ETH3D but 4.99 px on Middlebury). Qualitative evaluation on the Tree Branches dataset establishes DEFOM as the gold-standard baseline for vegetation depth estimation, with superior cross-domain consistency (consistently ranking 1st-2nd across benchmarks, average rank 1.75). DEFOM predictions will serve as pseudo-ground-truth for future benchmarking.

翻译：自主无人机林业作业需要具备强大跨域泛化能力的鲁棒深度估计，然而现有评估主要集中于城市和室内场景，在植被密集环境中存在关键空白。我们首次对涵盖迭代优化、基础模型、扩散模型和3D CNN范式的八种立体方法进行了系统性零样本评估。所有方法均使用官方发布的预训练权重（基于Scene Flow数据集训练），并在四个标准基准（ETH3D、KITTI 2012/2015、Middlebury）及包含5,313对图像的新型坎特伯雷树枝数据集（$1920 \times 1080$）上进行测试。结果揭示了场景依赖性规律：基础模型在结构化场景表现优异（BridgeDepth在ETH3D上误差为0.23像素；DEFOM在Middlebury上误差为4.65像素），而迭代方法在不同基准间呈现波动性能（IGEV++在ETH3D上误差为0.36像素但在Middlebury上为6.77像素；IGEV在ETH3D上误差为0.33像素但在Middlebury上为4.99像素）。在树枝数据集上的定性评估确立了DEFOM作为植被深度估计的黄金标准基线，其展现出卓越的跨域一致性（在所有基准中持续位列第1-2名，平均排名1.75）。DEFOM的预测结果将作为未来基准测试的伪真值参考。