Learning Visible Surface Area Estimation for Irregular Objects

Visible surface area estimation for irregular objects, one of the most fundamental and challenging topics in mathematics, supports a wide range of applications. The existing techniques usually estimate the visible surface area via mathematical modeling from 3D point clouds. However, the 3D scanner is expensive, and the corresponding evaluation method is too complex. In this paper, we propose a novel problem setting, deep learning for visible surface area estimation, which is the first trial to estimate the visible surface area for irregular objects from monocular images. Technically, we first build a novel visible surface area estimation dataset including 9099 real annotations. Then, we design a learning-based architecture to predict the visible surface area, including two core modules (i.e., the classification module and the area-bins module). The classification module is presented to predict the visible surface area distribution interval and assist network training for more accurate visible surface area estimation. Meanwhile, the area-bins module using the transformer encoder is proposed to distinguish the difference in visible surface area between irregular objects of the same category. The experimental results demonstrate that our approach can effectively estimate the visible surface area for irregular objects with various categories and sizes. We hope that this work will attract further research into this newly identified, yet crucial research direction. Our source code and data are available at https://github.com/liuxu0303/VSAnet .