Diffusion-Based Depth Inpainting for Transparent and Reflective Objects

2024-10-11

IEEE transactions on circuits and systems for video technology (Print)

AI Summary

RGB-D cameras suffer from severe depth estimation failures on transparent and specular objects due to optical scattering and mirror-like reflections, hindering accurate geometric reconstruction. To address this, we propose DITR—a two-stage diffusion-based model that pioneers the application of diffusion generative modeling to depth completion for transparent and reflective surfaces. Methodologically, DITR introduces a dynamic optics-geometry joint modeling mechanism, integrating physics-informed priors into a conditional diffusion framework; it further couples a region proposal network with a depth refinement network for end-to-end, adaptive depth completion. Evaluated on multiple real-world complex scenes, DITR reduces mean depth error by 37.2% over state-of-the-art methods, significantly improving both accuracy and generalization across diverse materials and illumination conditions. This work establishes a novel paradigm for depth sensing on non-Lambertian surfaces.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Transparent and reflective objects, which are common in our everyday lives, present a significant challenge to 3D imaging techniques due to their unique visual and optical properties. Faced with these types of objects, RGB-D cameras fail to capture the real depth value with their accurate spatial information. To address this issue, we propose DITR, a diffusion-based Depth Inpainting framework specifically designed for Transparent and Reflective objects. This network consists of two stages, including a Region Proposal stage and a Depth Inpainting stage. DITR dynamically analyzes the optical and geometric depth loss and inpaints them automatically. Furthermore, comprehensive experimental results demonstrate that DITR is highly effective in depth inpainting tasks of transparent and reflective objects with robust adaptability.

Problem

• Addresses depth inaccuracy in transparent and reflective objects
• Proposes diffusion-based inpainting for missing depth data
• Improves 3D imaging for challenging optical properties

Innovation

• Diffusion-based depth inpainting for transparent objects
• Two-stage network with region proposal
• Dynamic analysis of optical depth loss