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.

翻译：我们提出了一种新颖的方法，利用自然光照条件下手持拍摄的图像重建透明物体的3D形状。该方法结合了显式网格与多层感知器（MLP）网络的优点，采用混合表示形式，简化了近期研究中使用的采集设置。通过多视角轮廓获得初始形状后，我们引入基于表面的局部MLP来编码顶点位移场（VDF），以重建表面细节。局部MLP的设计允许使用两层MLP网络以分段方式表示VDF，这有利于优化算法。将局部MLP定义在表面而非体素上，也减小了搜索空间。这种混合表示使我们能够将表示光路约束的射线-像素对应关系松弛为设计的射线-胞元对应关系，从而显著简化了基于单图像的环境抠图算法的实现。我们使用多个具有真实模型的透明物体评估了所提出的表示方法与重建算法。实验表明，在简化数据采集设置的情况下，我们的方法能够产生优于现有技术的高质量重建结果。