3D Representation Methods: A Survey

TL;DR

This survey reviews the development, key techniques, datasets, and future directions of 3D representation methods, emphasizing their importance in applications like graphics, VR, and autonomous systems.

Contribution

It provides a comprehensive overview of current 3D representation techniques, datasets, and identifies promising future research directions.

Findings

Key techniques include Voxel Grid, Point Cloud, Mesh, SDF, NeRF, and DMTet.

Highlights the impact of datasets on research progress.

Discusses strengths, weaknesses, and future opportunities in 3D representation.

Abstract

The field of 3D representation has experienced significant advancements, driven by the increasing demand for high-fidelity 3D models in various applications such as computer graphics, virtual reality, and autonomous systems. This review examines the development and current state of 3D representation methods, highlighting their research trajectories, innovations, strength and weakness. Key techniques such as Voxel Grid, Point Cloud, Mesh, Signed Distance Function (SDF), Neural Radiance Field (NeRF), 3D Gaussian Splatting, Tri-Plane, and Deep Marching Tetrahedra (DMTet) are reviewed. The review also introduces essential datasets that have been pivotal in advancing the field, highlighting their characteristics and impact on research progress. Finally, we explore potential research directions that hold promise for further expanding the capabilities and applications of 3D representation methods.