3D 生成技術的進展：一項調查

摘要

在電腦圖形學中，生成3D模型是核心內容，也是數十年研究的焦點。隨著先進神經表示和生成模型的出現，3D內容生成領域正在迅速發展，使得能夠創建越來越高質量和多樣化的3D模型。這一領域的快速增長使得跟上所有最新發展變得困難。在這份調查中，我們旨在介紹3D生成方法的基本方法論，並建立一個結構化路線圖，包括3D表示、生成方法、數據集和相應應用。具體而言，我們介紹作為3D生成基礎的3D表示。此外，我們提供了對生成方法快速增長文獻的全面概述，按照算法範式類型進行分類，包括前饋生成、基於優化的生成、程序化生成和生成新視圖合成。最後，我們討論可用的數據集、應用和開放挑戰。我們希望這份調查能幫助讀者探索這一激動人心的主題，並促進3D內容生成領域的進一步發展。

English

Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.

3D 生成技術的進展：一項調查

Advances in 3D Generation: A Survey

摘要

Support