Recent advances in discriminative and generative pretraining have yielded geometry estimation models with strong generalization capabilities. While discriminative monocular geometry estimation methods rely on large-scale fine-tuning data to achieve zero-shot generalization, several generative-based paradigms show the potential of achieving impressive generalization performance on unseen scenes by leveraging pre-trained diffusion models and fine-tuning on even a small scale of synthetic training data. Frustratingly, these models are trained with different recipes on different datasets, making it hard to find out the critical factors that determine the evaluation performance. Besides, current geometry evaluation benchmarks have two main drawbacks that may prevent the development of the field, i.e., limited scene diversity and unfavorable label quality. To resolve the above issues, (1) we build fair and strong baselines in a unified codebase for evaluating and analyzing the geometry estimation models; (2) we evaluate monocular geometry estimators on more challenging benchmarks for geometry estimation task with diverse scenes and high-quality annotations. Our results reveal that pre-trained using large data, discriminative models such as DINOv2, can outperform generative counterparts with a small amount of high-quality synthetic data under the same training configuration, which suggests that fine-tuning data quality is a more important factor than the data scale and model architecture. Our observation also raises a question: if simply fine-tuning a general vision model such as DINOv2 using a small amount of synthetic depth data produces SOTA results, do we really need complex generative models for depth estimation? We believe this work can propel advancements in geometry estimation tasks as well as a wide range of downstream applications.

翻译：近年来，判别式与生成式预训练技术的进步催生了具有强大泛化能力的几何估计模型。尽管判别式单目几何估计方法依赖大规模微调数据以实现零样本泛化，但若干基于生成式的方法通过利用预训练的扩散模型，并仅在小规模合成训练数据上进行微调，已展现出在未见场景上取得优异泛化性能的潜力。令人困扰的是，这些模型采用不同的训练方案在不同数据集上进行训练，导致难以确定影响评估性能的关键因素。此外，当前几何评估基准存在两大可能阻碍领域发展的缺陷：场景多样性不足与标注质量欠佳。为解决上述问题，（1）我们在统一代码库中构建了公平且强大的基线，用于评估与分析几何估计模型；（2）我们在更具挑战性的几何估计任务基准上评估单目几何估计器，这些基准涵盖多样化场景并提供高质量标注。我们的结果表明：在相同训练配置下，使用大规模数据预训练的判别式模型（如DINOv2）能够超越仅使用少量高质量合成数据训练的生成式模型，这表明微调数据的质量是比数据规模和模型架构更重要的因素。我们的观察也引出一个问题：若仅使用少量合成深度数据微调通用视觉模型（如DINOv2）即可产生最先进的结果，我们是否真的需要复杂的生成式模型进行深度估计？我们相信这项工作将推动几何估计任务及广泛下游应用的进步。