MonoRelief: Recovering 2.5D Relief From a Single Image

Area of Science:

• Computer Vision and Graphics
• Image Processing and Analysis
• 3D Reconstruction and Rendering

Background:

• Single-image relief recovery is challenging due to inherent depth ambiguity.
• Existing methods often struggle with intricate geometric details and diverse material properties.
• Accurate depth and normal map estimation are crucial for high-fidelity relief reconstruction.

Purpose of the Study:

• To introduce MonoRelief, a novel method for high-quality 2.5D relief recovery from a single image.
• To develop a robust normal estimation network trained on a large-scale, diverse relief dataset.
• To integrate depth map generation with normal map estimation for improved relief reconstruction.

Main Methods:

• Construction of a large-scale relief dataset covering varied shapes, materials, and lighting.
• Training a robust normal estimation network on the curated relief dataset.
• Leveraging DepthAnything v2 for state-of-the-art depth map generation from input images.
• Integrating estimated normal maps and generated depth maps for comprehensive relief recovery.

Main Results:

• MonoRelief successfully recovers 2.5D reliefs with accurate depth structures and fine geometric details.
• The trained normal estimation network demonstrates robustness across diverse relief image types.
• Experimental validation confirms the effectiveness and robustness of the proposed MonoRelief method.

Conclusions:

• MonoRelief offers a significant advancement in single-image relief recovery by synergistically combining depth and normal information.
• The method shows strong potential for various downstream applications, including Image-to-Relief and Text-to-Relief.
• Future work can explore further enhancements in relief reproduction and real-world application integration.