Image Matting and 3D Reconstruction in One Loop

Xinshuang Liu, Siqi Li, Yue Gao

Abstract: Recent 3D object reconstruction methods rely on user-input alpha mattes to remove the background and reconstruct the object, because automatically predicted alpha mattes are not accurate enough. To realize automatic 3D object reconstruction, we propose a Joint framework for image Matting and 3D object Reconstruction (JointMR). It iteratively integrates information from all images into object hint maps to help image matting models predict better alpha mattes for each image and, in turn, improves 3D object reconstruction performance. The convergence of our framework is theoretically guaranteed. We further propose a method to convert an arbitrary image matting model into its hint-based counterpart. We conduct experiments on 3D object reconstruction from multi-view images and 3D dynamic object reconstruction from monocular videos. Different combinations of 3D object reconstruction models and image matting models are also tested. Experimental results show that our framework only slightly increases the computation cost but significantly improves the performance of all model combinations, demonstrating its compatibility and efficiency. Our code, models, and data are available at https://github.com/XinshuangL/JointMR.