MonoRelief: Recovering 2.5D Relief from a Single Image.

Abstract

In this paper, we introduce MonoRelief, a novel method that combines the strengths of a depth map and a normal map to achieve high-quality relief recovery from a single image. By constructing a large-scale relief dataset that encompasses a diverse range of relief shapes, materials, and lighting conditions, we enable the training of a robust normal estimation network capable of handling various types of relief images. Furthermore, we leverage the state-of-the-art method, DepthAnything v2 [1], to generate depth maps from the input images. By integrating the strengths of both maps, MonoRelief recovers 2.5D reliefs with reasonable depth structures and intricate geometrical details. We validate the effectiveness and robustness of MonoRelief through comprehensive experiments, and showcase its potential in a variety of downstream applications, including Image-to-Relief, Text-to-Relief, Lines-to-Relief and relief reproduction.