In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) representation of a class of shapes that enables high quality shape representation, interpolation and completion from partial and noisy 3D input data.
With the advent of deep neural networks, learning-based approaches for 3D reconstruction have gained popularity.
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking.
Generative models for 3D geometric data arise in many important applications in 3D computer vision and graphics.
In this network, a Score Generation Unit is devised to evaluate the quality of each projected image with score vectors.