Vision-Language Embodiment for Monocular Depth Estimation

2025-03-18

AI Summary

Monocular depth estimation suffers from high uncertainty and poor generalization due to neglect of intrinsic camera geometry. To address this, we propose an embodied camera modeling–driven cross-modal depth estimation framework. Our method explicitly embeds camera intrinsics into the network architecture to ensure physically consistent depth inference; fuses RGB images with depth-aware textual priors to construct vision-language collaborative representations; and incorporates real-time geometric reasoning and cross-modal contrastive learning for adaptive inference in dynamic road environments. The approach requires no additional hardware and enables end-to-end, real-time, robust, and interpretable depth prediction. Evaluated on multiple benchmarks, it achieves significant improvements in accuracy—e.g., a 12.3% reduction in relative error (ΔRel) on KITTI—and markedly enhances cross-scene generalization capability.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the camera itself. We propose a method that embodies the camera model and its physical characteristics into a deep learning model, computing embodied scene depth through real-time interactions with road environments. The model can calculate embodied scene depth in real-time based on immediate environmental changes using only the intrinsic properties of the camera, without any additional equipment. By combining embodied scene depth with RGB image features, the model gains a comprehensive perspective on both geometric and visual details. Additionally, we incorporate text descriptions containing environmental content and depth information as priors for scene understanding, enriching the model's perception of objects. This integration of image and language - two inherently ambiguous modalities - leverages their complementary strengths for monocular depth estimation. The real-time nature of the embodied language and depth prior model ensures that the model can continuously adjust its perception and behavior in dynamic environments. Experimental results show that the embodied depth estimation method enhances model performance across different scenes.

Problem

• Monocular depth estimation faces inherent uncertainties from single images
• Current models overlook camera intrinsic information in supervised training
• Integrating camera physics and language improves real-time depth accuracy

Innovation

• Embodies camera model into deep learning
• Combines RGB and text for depth estimation
• Uses real-time environmental interactions