用自回归方法实现可扩展的单目深度估计，性能超越现有模型。
Scalable Autoregressive Monocular Depth Estimation
- 将深度图分块处理为序列，逐层生成高分辨率深度图。
- 在KITTI上达到1.799的RMSE，比当前最优提升5%。
- 支持零样本迁移，适合想接入大模型的开发者使用。
本文表明，自回归模型是高效且可扩展的单目深度估计方法。核心思路是：将不同分辨率的深度图视为一系列标记，通过分块因果掩码实现从低到高的自回归生成；同时将完整深度范围递归离散化为更紧凑区间，以序数回归方式实现粗到细的自回归目标。结合这两种机制，DAR在KITTI和NYU Depth v2上均取得新SOTA，尤其在KITTI上实现1.799的RMSE（较当前最优的Depth Anything提升5%），并可扩展至20亿参数规模。此外，DAR在未见过的数据集上展现出零样本泛化能力。结果表明，自回归范式能有效提升深度估计性能，为将GPT-4o等现代大模型赋予深度感知能力提供了可行路径。
原文摘要 · Abstract (English)
This paper shows that the autoregressive model is an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. First, our depth autoregressive model (DAR) treats the depth map of different resolutions as a set of tokens, and conducts the low-to-high resolution autoregressive objective with a patch-wise casual mask. Second, our DAR recursively discretizes the entire depth range into more compact intervals, and attains the coarse-to-fine granularity autoregressive objective in an ordinal-regression manner. By coupling these two autoregressive objectives, our DAR establishes new state-of-the-art (SOTA) on KITTI and NYU Depth v2 by clear margins. Further, our scalable approach allows us to scale the model up to 2.0B and achieve the best RMSE of 1.799 on the KITTI dataset (5% improvement) compared to 1.896 by the current SOTA (Depth Anything). DAR further showcases zero-shot generalization ability on unseen datasets. These results suggest that DAR yields superior performance with an autoregressive prediction paradigm, providing a promising approach to equip modern autoregressive large models (e.g., GPT-4o) with depth estimation capabilities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。