A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing
  Objects in 3D Scenes
Ting Yu Xiaojun Lin Shuhui Wang Weiguo Sheng Qingming Huang Jun Yu
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.