Deep Implicit Surface Point Prediction Networks

TL;DR

This paper introduces a novel implicit surface representation called CSP that accurately models complex open and closed 3D shapes, enabling efficient computation of geometric properties and improved rendering and meshing.

Contribution

The paper proposes the CSP implicit representation, allowing high-fidelity modeling of arbitrary topology surfaces with efficient geometric property computation.

Findings

Outperforms state-of-the-art methods on ShapeNet dataset

Enables accurate normal and tangent estimation for open and closed shapes

Facilitates efficient surface rendering and mesh extraction

Abstract

Deep neural representations of 3D shapes as implicit functions have been shown to produce high fidelity models surpassing the resolution-memory trade-off faced by the explicit representations using meshes and point clouds. However, most such approaches focus on representing closed shapes. Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes. However, since the gradients of UDFs vanish on the surface, it is challenging to estimate local (differential) geometric properties like the normals and tangent planes which are needed for many downstream applications in vision and graphics. There are additional challenges in computing these properties efficiently with a low-memory footprint. This paper presents a novel approach that models such surfaces using a new class of implicit representations called the closest surface-point (CSP) representation. We show that CSP allows us to represent complex surfaces of any topology (open or closed) with high fidelity. It also allows for accurate and efficient computation of local geometric properties. We further demonstrate that it leads to efficient implementation of downstream algorithms like sphere-tracing for rendering the 3D surface as well as to create explicit mesh-based representations. Extensive experimental evaluation on the ShapeNet dataset validate the above contributions with results surpassing the state-of-the-art.