Diffusion-Based Depth Inpainting for Transparent and Reflective Objects
This addresses a specific challenge in computer vision for applications like robotics and AR/VR, though it appears incremental as it builds on existing diffusion and inpainting methods for a niche domain.
The paper tackles the problem of 3D imaging for transparent and reflective objects, where RGB-D cameras fail to capture accurate depth values, by proposing DITR, a diffusion-based depth inpainting framework that dynamically analyzes optical and geometric depth loss and automatically inpaints them, demonstrating high effectiveness in depth inpainting tasks with robust adaptability.
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.