Three-Dimensional (3D) dense captioning is an emerging vision-language
bridging task that aims to generate multiple detailed and accurate descriptions
for 3D scenes. It presents significant potential and challenges due to its
closer representation of the real world compared to 2D visual captioning, as
well as complexities in data collection and processing of 3D point cloud
sources. Despite the popularity and success of existing methods, there is a
lack of comprehensive surveys summarizing the advancements in this field, which
hinders its progress. In this paper, we provide a comprehensive review of 3D
dense captioning, covering task definition, architecture classification,
dataset analysis, evaluation metrics, and in-depth prosperity discussions.
Based on a synthesis of previous literature, we refine a standard pipeline that
serves as a common paradigm for existing methods. We also introduce a clear
taxonomy of existing models, summarize technologies involved in different
modules, and conduct detailed experiment analysis. Instead of a chronological
order introduction, we categorize the methods into different classes to
facilitate exploration and analysis of the differences and connections among
existing techniques. We also provide a reading guideline to assist readers with
different backgrounds and purposes in reading efficiently. Furthermore, we
propose a series of promising future directions for 3D dense captioning by
identifying challenges and aligning them with the development of related tasks,
offering valuable insights and inspiring future research in this field. Our aim
is to provide a comprehensive understanding of 3D dense captioning, foster
further investigations, and contribute to the development of novel applications
in multimedia and related domains.

3D 密集的图像描述是一个新兴的视觉 - 语言桥接任务，旨在为 3D 场景生成多个详细准确的描述。本文提供了一份综合性的 3D 密集图像描述综述，涵盖了任务定义、架构分类、数据集分析、评估指标和深入的繁荣讨论。通过综合以前的文献，我们提炼了一个作为现有方法共同范例的标准流程，并提出了一种清晰的现有模型分类方法，并总结了不同模块中涉及的技术，并进行了详细的实验分析。同时，我们也提出了一系列有前景的 3D 密集图像描述的未来方向，并将其与相关任务的发展相结合，为这一领域的未来研究提供有价值的见解和启发。我们的目标是提供对 3D 密集图像描述的全面理解，促进进一步的研究，并在多媒体和相关领域的新应用的发展中做出贡献。