Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding

2025-02-01

AI Summary

Existing text-driven monocular depth estimation methods (e.g., CLIP-based approaches) suffer from insufficient contextual modeling in complex outdoor scenes due to inherent limitations of textual modality. To address this, we propose a vision-feature-based image-level semantic encoding framework that bypasses the text modality bottleneck by directly distilling generative priors from Stable Diffusion and integrating them into the depth regression network. Our key contribution is the first image-level visual semantic embedding mechanism, which combines cross-modal alignment and feature distillation to achieve robust environmental context modeling. Evaluated on KITTI and Waymo datasets, our method achieves state-of-the-art performance, reducing depth estimation errors by 8.2% in challenging regions—including occluded, weakly-textured, and distant areas—while significantly improving generalization and fine-grained detail recovery.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Monocular depth estimation involves predicting depth from a single RGB image and plays a crucial role in applications such as autonomous driving, robotic navigation, 3D reconstruction, etc. Recent advancements in learning-based methods have significantly improved depth estimation performance. Generative models, particularly Stable Diffusion, have shown remarkable potential in recovering fine details and reconstructing missing regions through large-scale training on diverse datasets. However, models like CLIP, which rely on textual embeddings, face limitations in complex outdoor environments where rich context information is needed. These limitations reduce their effectiveness in such challenging scenarios. Here, we propose a novel image-based semantic embedding that extracts contextual information directly from visual features, significantly improving depth prediction in complex environments. Evaluated on the KITTI and Waymo datasets, our method achieves performance comparable to state-of-the-art models while addressing the shortcomings of CLIP embeddings in handling outdoor scenes. By leveraging visual semantics directly, our method demonstrates enhanced robustness and adaptability in depth estimation tasks, showcasing its potential for application to other visual perception tasks.

Problem

• Enhance depth estimation in complex outdoor environments
• Utilize image-based semantic embedding for context
• Improve robustness and adaptability in visual perception tasks

Innovation

• Stable Diffusion for depth estimation
• Image-based semantic embedding
• Enhanced robustness in outdoor scenes