mlsanityData to 14 Oct 2026

The Third Monocular Depth Estimation Challenge

arXiv 2404.16831 ↗First posted 25 Apr 2024cs.CVcited by 10

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Abstract (arXiv)

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 99 of its 126 references that are in the map. First posted on arXiv 25 Apr 2024.

Builds on

99 of 126 references.

References · 99

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38 more
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50 most cited of 99 All 99 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 1.

10 papers.

  1. 2024Depth Anything V2cited by 1,422
  2. 2024Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditionscited by 29
  3. 2025LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoningcited by 8
  4. 2025NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfacescited by 3
  5. 2024NimbleD: Enhancing Self-supervised Monocular Depth Estimation with Pseudo-labels and Large-scale Video Pre-trainingcited by 2
  6. 2024Refinement of Monocular Depth Maps via Multi-View Differentiable Renderingcited by 1
  7. 2024Single-Shot Metric Depth from Focused Plenoptic Camerascited by 1
  8. 2025ROVR-Open-Dataset: A Large-Scale Depth Dataset for Autonomous Drivingcited by 1
  9. 2026ZipDepth: Bringing Lightweight Zero-Shot Monocular Depth Anywhere, on Any Devicecited by 1
  10. 2025Survey on Monocular Metric Depth Estimationcited by 0