Neural 3D reconstruction from sparse views using geometric priors

Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D representation.Existing methods usually only make use of 2D views,requiring a dense set of input views for accurate 3D reconstruction.In this paper,we show that accurate 3D reconstruction can be achieved by incorporating geometric priors into neural implicit 3D reconstruction.Our method adopts the signed distance function as the 3D representation,and learns a generalizable 3D surface reconstruction model from sparse views.Specifically,we build a more effective and sparse feature volume from the input views by using corresponding depth maps,which can be provided by depth sensors or directly predicted from the input views.We recover better geometric details by imposing both depth and surface normal constraints in addition to the color loss when training the neural implicit 3D representation.Experiments demonstrate that our method both outperforms state-of-the-art approaches,and achieves good generalizability.

MDISN:Learning multiscale deformed implicit fields from single images

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.