MDISN: Learning multiscale deformed implicit fields from single images

Abstract

We present a multiscale deformed implicit surface network (MDISN) to reconstruct 3D objects from single images by adapting the implicit surface of the target object from coarse to fine to the input image. The basic idea is to optimize the implicit surface according to the change of consecutive feature maps from the input image. And with multi-resolution feature maps, the implicit field is refined progressively, such that lower resolutions outline the main object components, and higher resolutions reveal fine-grained geometric details. To better explore the changes in feature maps, we devise a simple field deformation module that receives two consecutive feature maps to refine the implicit field with finer geometric details. Experimental results on both synthetic and real-world datasets demonstrate the superiority of the proposed method compared to state-of-the-art methods.

Keywords

• Single-view 3D reconstruction
• Implicit neural representation
• Multiscale deformation