mlsanityData to 14 Oct 2026

PureCLIP-Depth: Prompt-Free and Decoder-Free Monocular Depth Estimation within CLIP Embedding Space

arXiv 2603.16238 ↗First posted 17 Mar 2026cs.CVcited by 1

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Abstract (arXiv)

We propose PureCLIP-Depth, a completely prompt-free, decoder-free Monocular Depth Estimation (MDE) model that operates entirely within the Contrastive Language-Image Pre-training (CLIP) embedding space. Unlike recent models that rely heavily on geometric features, we explore a novel approach to MDE driven by conceptual information, performing computations directly within the conceptual CLIP space. The core of our method lies in learning a direct mapping from the RGB domain to the depth domain strictly inside this embedding space. Our approach achieves state-of-the-art performance among CLIP embedding-based models on both indoor and outdoor datasets. The code used in this research is available at: https://github.com/ryutaroLF/PureCLIP-Depth

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 26 references that are in the map. First posted on arXiv 17 Mar 2026.

Builds on

21 of 26 references.

References · 21

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2021Vision Transformers for Dense Predictioncited by 1,803
  4. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  6. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  7. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
  8. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  9. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 382
  10. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  11. 2019Learning the Depths of Moving People by Watching Frozen Peoplecited by 120
  12. 2022Can Language Understand Depth?cited by 69
9 more
  1. 2024WorDepth: Variational Language Prior for Monocular Depth Estimationcited by 34
  2. 2023Learning to Adapt CLIP for Few-Shot Monocular Depth Estimationcited by 20
  3. 2025Vision-Language Embodiment for Monocular Depth Estimationcited by 16
  4. 2024Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Spacecited by 10
  5. 2024CLIP Can Understand Depthcited by 7
  6. 2024On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimationcited by 4
  7. 2024Language-Based Depth Hints for Monocular Depth Estimationcited by 3
  8. 2024Large Language Models Can Understanding Depth from Monocular Imagescited by 3
  9. 2025Language-Depth Navigated Thermal and Visible Image Fusioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimationcited by 0