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Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding

Jingming Xia, Guanqun Cao, Guang Ma, Yiben Luo, Qinzhao Li, John Oyekan

TL;DR

This work tackles monocular depth estimation in challenging outdoor environments by replacing text-based semantic guidance with an image-derived semantic encoding. The authors integrate a frozen latent feature extractor with a spatially enhanced SeeCoder semantic encoder and a cross-attention-enabled denoising UNet inside a Stable Diffusion framework, followed by a task-specific decoder to produce high-resolution depth maps. Evaluations on KITTI and Waymo show competitive accuracy and robustness, with SeeCoder-based semantics offering advantages over CLIP in outdoor scenes, though textureless regions remain challenging. The approach demonstrates strong generalization potential and suggests applicability to other visual perception tasks beyond depth estimation.

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.

Leveraging Stable Diffusion for Monocular Depth Estimation via Image Semantic Encoding

TL;DR

This work tackles monocular depth estimation in challenging outdoor environments by replacing text-based semantic guidance with an image-derived semantic encoding. The authors integrate a frozen latent feature extractor with a spatially enhanced SeeCoder semantic encoder and a cross-attention-enabled denoising UNet inside a Stable Diffusion framework, followed by a task-specific decoder to produce high-resolution depth maps. Evaluations on KITTI and Waymo show competitive accuracy and robustness, with SeeCoder-based semantics offering advantages over CLIP in outdoor scenes, though textureless regions remain challenging. The approach demonstrates strong generalization potential and suggests applicability to other visual perception tasks beyond depth estimation.

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.

Paper Structure

This paper contains 19 sections, 2 equations, 3 figures, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Related Work
  3. Generative Adversarial Networks
  4. Diffusion Models
  5. Semantic Embeddings in Diffusion Models
  6. Spatial Enhancement Techniques
  7. Methodology
  8. Latent Features Extraction
  9. Spatially Enhanced SeeCoder
  10. Denoising UNet
  11. Task-Specific Decoder
  12. EXPERIMENTS
  13. Outdoor Datasets
  14. Evaluation Metric
  15. Training and Evaluation on KITTI
  16. ...and 4 more sections

Figures (3)

  • Figure 1: The overview of our proposed framework: an input RGB image is processed through two parallel pathways, an Image Encoder and an Image Semantic Encoder. The Image Encoder extracts latent features, with its weights frozen during training and inference, while the Image Semantic Encoder produces a semantic vector that conditions these features. Both are integrated within a denoising UNet that fuses these modalities to produce multi-scale feature maps. Subsequently, a decoder upsamples these feature maps, generating the final metric depth map.
  • Figure 2: The visualization of selected samples from the KITTI dataset. Several samples are shown, with each column from left to right representing the RGB image, the sparse ground truth depth map after visualization processing, and the dense depth map predicted by our model.
  • Figure 3: The visualization of model prediction differences across three different scenarios: normal daylight conditions, rainy weather, and nighttime.