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

arXiv: https://arxiv.org/abs/2603.16238 · HTML: https://mlsanity.com/paper/2603.16238/

## Key facts

- Cited by 1 papers 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 (most cited first, 21 of 21)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Learning the Depths of Moving People by Watching Frozen People](https://mlsanity.com/paper/1904.11111/) (2019)
- [Can Language Understand Depth?](https://mlsanity.com/paper/2207.01077/) (2022)
- [WorDepth: Variational Language Prior for Monocular Depth Estimation](https://mlsanity.com/paper/2404.03635/) (2024)
- [Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation](https://mlsanity.com/paper/2311.01034/) (2023)
- [Vision-Language Embodiment for Monocular Depth Estimation](https://mlsanity.com/paper/2503.16535/) (2025)
- [Infrared and Visible Image Fusion with Language-Driven Loss in CLIP Embedding Space](https://mlsanity.com/paper/2402.16267/) (2024)
- [CLIP Can Understand Depth](https://mlsanity.com/paper/2402.03251/) (2024)
- [On the Robustness of Language Guidance for Low-Level Vision Tasks: Findings from Depth Estimation](https://mlsanity.com/paper/2404.08540/) (2024)
- [Language-Based Depth Hints for Monocular Depth Estimation](https://mlsanity.com/paper/2403.15551/) (2024)
- [Large Language Models Can Understanding Depth from Monocular Images](https://mlsanity.com/paper/2409.01133/) (2024)
- [Language-Depth Navigated Thermal and Visible Image Fusion](https://mlsanity.com/paper/2503.08676/) (2025)

## Built on by (most cited first, 1 of 1)

- [SPACE-CLIPv2: Decoding Local Geometry from Frozen CLIP for Monocular Depth Estimation](https://mlsanity.com/paper/2610.05029/) (2026)

## 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