**ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation**
**Authors:** Suraj Patni, Aradhya Agarwal, Chetan Arora
**Institution:** Indian Institute of Technology Delhi
**GitHub:** [https://ecodepth-iitd.github.io](https://ecodepth-iitd.github.io)
**Abstract:**
This paper addresses the challenge of monocular depth estimation (SIDE) using a learning-based approach. Traditional methods rely heavily on shading and contextual cues, which are limited in their effectiveness. To improve performance, the authors propose using global image priors generated from a pre-trained Vision Transformer (ViT) model to provide more detailed contextual information. The proposed model uses a diffusion backbone conditioned on ViT embeddings, achieving state-of-the-art (SOTA) performance on the NYU Depth v2 and KITTI datasets. The model outperforms existing methods by 14% and 2% in absolute relative error and square relative error, respectively. Additionally, the model demonstrates superior zero-shot transfer capabilities, outperforming current SOTA methods by significant margins.
**Contributions:**
1. A new SIDE model using a diffusion backbone conditioned on ViT embeddings, achieving SOTA performance on benchmark datasets.
2. Demonstrating that using ViT embeddings for semantic context is more effective than generating pseudo captions and using CLIP embeddings.
3. Showcasing improved zero-shot transfer performance with a model trained on a single dataset.
**Related Work:**
- Traditional methods rely on feature correspondence, parallax, and triangulation from multiple views.
- Deep learning techniques use CNNs and transformer-based architectures for dense regression.
- Recent works use diffusion models pre-trained on large datasets for better convergence and performance.
**Proposed Methodology:**
- **Diffusion Model:** The model uses a latent diffusion process, where the input image is transformed into a latent space and then denoised to predict the depth map.
- **Comprehensive Image Detail Embedding (CIDE) Module:** This module extracts semantic context from ViT embeddings, providing richer information compared to pseudo captions.
- **Depth Regressor:** The final depth map is generated using a depth regressor applied to the feature maps from the UNet decoder.
**Experiments and Results:**
- **Benchmark Datasets:** The model achieves SOTA performance on the NYU Depth v2 and KITTI datasets.
- **Zero-Shot Transfer:** The model generalizes well to unseen datasets with minimal training, outperforming existing methods.
- **Ablation Study:** The effectiveness of contextual information and the impact of ViT architecture are evaluated.
**Conclusion:**
The proposed ECoDepth model effectively conditions diffusion models on ViT embeddings, improving monocular depth estimation and zero-shot transfer capabilities.**ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation**
**Authors:** Suraj Patni, Aradhya Agarwal, Chetan Arora
**Institution:** Indian Institute of Technology Delhi
**GitHub:** [https://ecodepth-iitd.github.io](https://ecodepth-iitd.github.io)
**Abstract:**
This paper addresses the challenge of monocular depth estimation (SIDE) using a learning-based approach. Traditional methods rely heavily on shading and contextual cues, which are limited in their effectiveness. To improve performance, the authors propose using global image priors generated from a pre-trained Vision Transformer (ViT) model to provide more detailed contextual information. The proposed model uses a diffusion backbone conditioned on ViT embeddings, achieving state-of-the-art (SOTA) performance on the NYU Depth v2 and KITTI datasets. The model outperforms existing methods by 14% and 2% in absolute relative error and square relative error, respectively. Additionally, the model demonstrates superior zero-shot transfer capabilities, outperforming current SOTA methods by significant margins.
**Contributions:**
1. A new SIDE model using a diffusion backbone conditioned on ViT embeddings, achieving SOTA performance on benchmark datasets.
2. Demonstrating that using ViT embeddings for semantic context is more effective than generating pseudo captions and using CLIP embeddings.
3. Showcasing improved zero-shot transfer performance with a model trained on a single dataset.
**Related Work:**
- Traditional methods rely on feature correspondence, parallax, and triangulation from multiple views.
- Deep learning techniques use CNNs and transformer-based architectures for dense regression.
- Recent works use diffusion models pre-trained on large datasets for better convergence and performance.
**Proposed Methodology:**
- **Diffusion Model:** The model uses a latent diffusion process, where the input image is transformed into a latent space and then denoised to predict the depth map.
- **Comprehensive Image Detail Embedding (CIDE) Module:** This module extracts semantic context from ViT embeddings, providing richer information compared to pseudo captions.
- **Depth Regressor:** The final depth map is generated using a depth regressor applied to the feature maps from the UNet decoder.
**Experiments and Results:**
- **Benchmark Datasets:** The model achieves SOTA performance on the NYU Depth v2 and KITTI datasets.
- **Zero-Shot Transfer:** The model generalizes well to unseen datasets with minimal training, outperforming existing methods.
- **Ablation Study:** The effectiveness of contextual information and the impact of ViT architecture are evaluated.
**Conclusion:**
The proposed ECoDepth model effectively conditions diffusion models on ViT embeddings, improving monocular depth estimation and zero-shot transfer capabilities.