Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/implicit solutions to this problem struggle to recover self-occluded geometry and/or faithfully reconstruct topological shape structures. To resolve this dilemma, we introduce LIST, a novel neural architecture that leverages local and global image features to accurately reconstruct the geometric and topological structure of a 3D object from a single image. We utilize global 2D features to predict a coarse shape of the target object and then use it as a base for higher-resolution reconstruction. By leveraging both local 2D features from the image and 3D features from the coarse prediction, we can predict the signed distance between an arbitrary point and the target surface via an implicit predictor with great accuracy. Furthermore, our model does not require camera estimation or pixel alignment. It provides an uninfluenced reconstruction from the input-view direction. Through qualitative and quantitative analysis, we show the superiority of our model in reconstructing 3D objects from both synthetic and real-world images against the state of the art.

利用 LIST 神经网络架构，借助局部和全局图像特征，精确地从单张图像中重建 3D 物体的几何和拓扑结构，既可以预测目标物体的粗糙形状，又能通过隐式预测器准确地预测任意点与目标表面之间的有向距离，模型在重建合成和真实世界图像中的 3D 物体方面表现出卓越的优势。

基于空间变换器的单视角三维重建中的隐性学习