mlsanityData to 14 Oct 2026

Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations

arXiv 2507.00981 ↗First posted 1 Jul 2025cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 38 of its 49 references that are in the map. First posted on arXiv 1 Jul 2025.

Builds on

38 of 49 references.

References · 38

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26 more
  1. 2020TartanAir: A Dataset to Push the Limits of Visual SLAMcited by 506
  2. 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 404
  3. 2024Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimationcited by 395
  4. 2024UniDepth: Universal Monocular Metric Depth Estimationcited by 382
  5. 20203D-FRONT: 3D Furnished Rooms with layOuts and semaNTicscited by 380
  6. 2022ProcTHOR: Large-Scale Embodied AI Using Procedural Generationcited by 380
  7. 2023Visual Adversarial Examples Jailbreak Aligned Large Language Modelscited by 328
  8. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 327
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  14. 2021Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Modelscited by 232
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  17. 2025UniDepthV2: Universal Monocular Metric Depth Estimation Made Simplercited by 173
  18. 2023Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereocited by 172
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  24. 2023RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptionscited by 49
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026ProcFunc: Function-Oriented Abstractions for Procedural 3D Generation in Pythoncited by 0
  2. 2026Can Single-View Mesh Reconstruction Generalize to Robot Camera Rotation?cited by 0
  3. 2026Geometric Collapse: When Vision Models Fail to Verify Physical Causalitycited by 0
  4. 2026Geometric Distillation from Rectified Stereo: Leveraging Epipolar Cues for Monocular Depthcited by 0