3D Shape Generation: A Survey

TL;DR

This survey comprehensively reviews recent progress in 3D shape generation using deep learning, covering representations, methods, datasets, and evaluation metrics, and discusses future challenges and directions.

Contribution

It provides a structured overview of current 3D shape generation techniques, categorizing representations and approaches, and highlighting open challenges for future research.

Findings

Categorization of 3D representations into explicit, implicit, and hybrid.

Summary of generative modeling approaches focusing on feedforward architectures.

Overview of datasets and evaluation metrics for 3D shape quality assessment.

Abstract

Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.