We propose a novel method to reconstruct the 3D shapes of transparent objects
using hand-held captured images under natural light conditions. It combines the
advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid
representation, to simplify the capture setting used in recent contributions.
After obtaining an initial shape through the multi-view silhouettes, we
introduce surface-based local MLPs to encode the vertex displacement field
(VDF) for the reconstruction of surface details. The design of local MLPs
allows to represent the VDF in a piece-wise manner using two layer MLP
networks, which is beneficial to the optimization algorithm. Defining local
MLPs on the surface instead of the volume also reduces the searching space.
Such a hybrid representation enables us to relax the ray-pixel correspondences
that represent the light path constraint to our designed ray-cell
correspondences, which significantly simplifies the implementation of
single-image based environment matting algorithm. We evaluate our
representation and reconstruction algorithm on several transparent objects with
ground truth models. Our experiments show that our method can produce
high-quality reconstruction results superior to state-of-the-art methods using
a simplified data acquisition setup.

本研究提出了一种基于手持拍摄的自然光下重建透明物体的 3D 形状的新方法，该方法采用了显式网格和多层感知器网络（MLP）的混合表示，通过多视图剪影获得初始形状，并使用基于表面的局部 MLP 编码顶点位移场（VDF）进行表面细节的重建。定义在表面而不是体积上的局部 MLPs 也减少了搜索空间，从而实现了基于单张图片的环境抠图算法的简化实现。经过多项实验证明，此方法可以在采集数据方面进行简化，并且可以确保重建质量优于现有的技术水平。