提出新基准，系统评估单目深度估计模型在可控扰动下的鲁棒性。
Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations
- 用程序化生成3D场景，模拟物体、相机、材质和光照变化
- 发现当前最优模型在材质与光照扰动下性能显著下降
- 适合关注模型鲁棒性与真实场景泛化能力的研究者
近年来，单目深度估计取得显著进展，尤其体现在大型模型在标准基准上的表现。然而，现有基准多侧重精度而忽视鲁棒性。本文提出PDE（Procedural Depth Evaluation）新基准，通过程序化生成3D场景，系统评估模型对物体、相机、材质和光照等可控扰动的鲁棒性。分析揭示了当前最优深度模型在特定扰动下的脆弱性，为后续研究提供了重要参考。代码与数据已公开于https://github.com/princeton-vl/proc-depth-eval。
原文摘要 · Abstract (English)
Recent years have witnessed substantial progress on monocular depth estimation, particularly as measured by the success of large models on standard benchmarks. However, performance on standard benchmarks does not offer a complete assessment, because most evaluate accuracy but not robustness. In this work, we introduce PDE (Procedural Depth Evaluation), a new benchmark which enables systematic robustness evaluation. PDE uses procedural generation to create 3D scenes that test robustness to various controlled perturbations, including object, camera, material and lighting changes. Our analysis yields interesting findings on what perturbations are challenging for state-of-the-art depth models, which we hope will inform further research. Code and data are available at https://github.com/princeton-vl/proc-depth-eval.
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