Deep Level Sets: Implicit Surface Representations for 3D Shape Inference

Abstract

Existing 3D surface representation approaches are unable to accurately classify pixels and their orientation lying on the boundary of an object. Thus resulting in coarse representations which usually require post-processing steps to extract 3D surface meshes. To overcome this limitation, we propose an end-to-end trainable model that directly predicts implicit surface representations of arbitrary topology by optimising a novel geometric loss function. Specifically, we propose to represent the output as an oriented level set of a continuous embedding function, and incorporate this in a deep end-to-end learning framework by introducing a variational shape inference formulation. We investigate the benefits of our approach on the task of 3D surface prediction and demonstrate its ability to produce a more accurate reconstruction compared to voxel-based representations. We further show that our model is flexible and can be applied to a variety of shape inference problems.

References in corpus (1)

• A Point Set Generation Network for 3D Object Reconstruction from a Single Image

Cited by in corpus (6)

• Neural Unsigned Distance Fields for Implicit Function Learning
• Analytic Marching: An Analytic Meshing Solution from Deep Implicit Surface Networks
• Learning Gradient Fields for Shape Generation
• Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Image
• A Simple and Scalable Shape Representation for 3D Reconstruction
• Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks