ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation (2403.18807v4)

Abstract: In the absence of parallax cues, a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive, it is necessary to train such models on large and varied datasets, which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models, such as CLIP, improves zero shot transfer in several applications. Taking inspiration from this, in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model, pre-trained on a large dataset, captures greater relevant information for SIDE than the usual route of generating pseudo image captions, followed by CLIP based text embeddings. Based on this idea, we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset, achieving Abs Rel error of 0.059 (14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset, achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2, we report mean relative improvement of (20%, 23%, 81%, 25%) over NeWCRFs on (Sun-RGBD, iBims1, DIODE, HyperSim) datasets, compared to (16%, 18%, 45%, 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.io

• The paper introduces ECoDepth, a novel diffusion-based architecture that conditions on ViT embeddings to enhance monocular depth estimation.
• It employs the Comprehensive Image Detail Embedding module to achieve a 14% improvement in Abs Rel error on the NYU Depth V2 benchmark.
• Experiments reveal strong generalizability with state-of-the-art zero-shot transfer performance across diverse datasets.

ECoDepth: Leveraging ViT Embeddings for Advanced Monocular Depth Estimation

Introduction

Monocular Depth Estimation (SIDE) has been a pivotal area of research in computer vision, offering critical insights for applications ranging from autonomous navigation to augmented reality. The core challenge lies in predicting depth from a single RGB image — a task traditionally approached through geometric techniques and, more recently, deep learning methods. However, the transition from geometric to data-driven approaches has introduced new dependencies, particularly on the diversity and volume of training data. This shift motivates the exploration of foundational models like Vision Transformers (ViT) for enhancing SIDE through detailed contextual embeddings.

Related Work

Recent developments in SIDE have been marked by the integration of Large Foundational Models (LFMs) and text-based embeddings to provide semantic context, thus improving model generalization and zero-shot capabilities. While prior research demonstrated the efficacy of pseudo-captions and CLIP embeddings for conditional guidance, our exploration suggests a more direct approach. We posit that utilizing the embeddings from a pre-trained ViT, without resorting to intermediate text generation, can capture more nuanced details relevant for SIDE. This perspective builds upon existing works in diffusion models and ViTs, proposing a novel use of ViT embeddings as direct conditioning for depth estimation models.

Proposed Methodology

Our approach, termed ECoDepth, introduces a diffusion-based architecture conditioned on embeddings derived from a pre-trained ViT model. This architecture is constructed upon the hypothesis that ViT embeddings, compared to textual descriptions, offer a richer and more comprehensive semantic understanding of the input image. Consequently, we design the Comprehensive Image Detail Embedding (CIDE) module, which employs ViT to extract global image priors and generate embeddings for conditioning the diffusion process.

A comparative analysis of using ViT embeddings against traditional pseudo-caption embeddings underscores our method's superior capability in capturing detailed scene information. The architecture effectively utilizes these embeddings to guide the diffusion process, resulting in significant improvements in depth estimation accuracy.

Experimental Results

Our evaluation on standard benchmarks, including the NYU Depth V2 and KITTI datasets, indicates that ECoDepth sets a new state-of-the-art in SIDE, achieving notable reductions in error metrics. For instance, on the NYU dataset, ECoDepth achieves a 14% improvement in Abs Rel error over the previous best model. Moreover, our model demonstrates exceptional generalizability, outperforming leading methods in zero-shot transfer tasks across a variety of datasets even when trained on a singular dataset.

Future Directions

The findings from ECoDepth open several avenues for future research in SIDE. The use of ViT embeddings as direct conditioning for the diffusion process underscores the potential of LFMs in enhancing model performance without relying on additional text-based intermediaries. Furthermore, the observed improvements in zero-shot transfer capability highlight the method's robustness and adaptability, suggesting that similar conditioning strategies could benefit other vision tasks beyond SIDE.

Conclusion

ECoDepth represents a substantial advancement in SIDE, leveraging the richer semantic context provided by ViT embeddings to condition the diffusion process. This approach not only raises the bar for depth estimation accuracy but also demonstrates the untapped potential of LFMs in improving generalization and zero-shot performance in computer vision tasks.