面向单目图像下三维目标检测与空间语义描述的统一建模

王天添; 郭柯宇; 罗函轲; 孙士杰; 程惠泽; 孙张龙

doi:10.12263/DZXB.20251129

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面向单目图像下三维目标检测与空间语义描述的统一建模

学术论文 | 更新时间：2026-06-16

- 面向单目图像下三维目标检测与空间语义描述的统一建模
- Towards Unified Modeling of 3D Detection and Spatial Semantic Captioning with Monocular Images
- 电子学报 2026年54卷第3期页码：1105-1117
- 作者机构：
  
  1.长安大学信息工程学院，陕西西安 710064
  2.长安大学数据科学与人工智能研究院，陕西西安 710064
- 作者简介：
  
  王天添男，2002年8月出生于湖南省娄底市。现为长安大学信息工程学院硕士研究生。主要研究方向为三维多目标检测、多模态学习。E-mail: wangtiantian@chd.edu.cn
  郭柯宇男，1999年9月出生于贵州省黔南布依族苗族自治州。现为长安大学信息工程学院博士研究生。主要研究方向为视觉定位、多模态学习。E-mail: nymph@uestc.edu.cn
  罗函轲女，2002年3月出生于陕西省咸阳市。现为长安大学信息工程学院硕士研究生。主要研究方向为3D目标检测、文本/图像到3D生成。E-mail: snowwhite@uestc.edu.cn
  孙士杰男，1989年10月出生于河南省商丘市。现为长安大学数据科学与人工智能研究院副教授、国际生博士生导师。主要研究方向为多目标检测跟踪、交通三维重建与多目标位姿估计。E-mail: shijieSun@chd.edu.cn
  程惠泽男，2002年11月出生于陕西省西安市。现为长安大学信息工程学院硕士研究生。主要研究方向为计算机视觉和多模态研究。E-mail: hzcheng@chd.edu.cn
  孙张龙男，2003年1月出生于安徽省安庆市。现为长安大学信息工程学院硕士研究生。主要研究方向为视觉语言分割。E-mail: 2024224109@chd.edu.cn
- 基金信息：
  
  国家重点研发计划(2023YFB4301800);国家自然科学基金(62576050);江西省青年基金(S2024QNJJL0062)
- DOI：10.12263/DZXB.20251129
  中图分类号： TP391.41;
- 收稿：2026-02-02，
  
  录用：2026-03-07，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
王天添, 郭柯宇, 罗函轲, 等. 面向单目图像下三维目标检测与空间语义描述的统一建模[J]. 电子学报, 2026, 54(03): 1105-1117.

WANG Tiantian, GUO Keyu, LUO Hanke, et al. Towards Unified Modeling of 3D Detection and Spatial Semantic Captioning with Monocular Images[J]. Acta Electronica Sinica, 2026, 54(03): 1105-1117.
王天添, 郭柯宇, 罗函轲, 等. 面向单目图像下三维目标检测与空间语义描述的统一建模[J]. 电子学报, 2026, 54(03): 1105-1117. DOI：10.12263/DZXB.20251129

WANG Tiantian, GUO Keyu, LUO Hanke, et al. Towards Unified Modeling of 3D Detection and Spatial Semantic Captioning with Monocular Images[J]. Acta Electronica Sinica, 2026, 54(03): 1105-1117. DOI：10.12263/DZXB.20251129

摘要

单目三维目标检测为三维感知提供了低成本解决方案，但现有方法难以生成可供人类直观理解的场景描述，从而限制了它们在人机交互、自动驾驶和其他需要丰富语义理解的场景中的适用性。视觉描述作为人类语言智能的直接体现，提供了理想的沟通媒介，赋予机器直观“讲述”场景的能力。但现有的视觉描述方法主要聚焦于单目图像内容，仅能表述物体间的二维拓扑关系，缺乏对三维几何信息（如精确距离、空间位置与运动状态）的精确建模与表达能力。若采用“先进行三维检测，再借助大模型生成描述”的两阶段方法，则存在系统效率低、信息一致性差的问题。而大模型的描述内容也只能局限于物体间的拓扑关系，无法准确反映三维几何信息，且因其固有的“幻觉”现象，也会导致空间信息的不准确并伴随冗余描述。为此，本文首次提出了单目三维视觉检测与空间语义描述（Monocular 3D Detection and Captioning，Mono3DDC）这一新颖任务。该任务旨在统一单目三维目标检测与描述生成，要求模型同时学习深度感知的视觉特征与语言语义，通过端到端的网络架构使生成的描述能够准确地学习到一致的三维空间信息，确保描述的几何准确性与三维目标检测的高精度。为支撑该交叉研究领域的深入探索，本文构建了首个支持中文语义的单目三维视觉描述基准数据集KITTI3DDC。该数据集基于KITTI数据集设计了一套高效的自动化数据生成流程，通过大语言模型与结构化验证模板的协同机制，在保证描述多样性与语言流畅性的同时，严格控制了空间信息的几何一致性，为后续研究提供了高质量的多模态监督信号。此外，本文设计了一个统一的新型框架，即Mono3DDC-TR（Monocular 3D Detection and Captioning based on Transformer）。该框架通过深度融合优化后的几何与视觉特征，在生成描述准确率及多类别三维检测效果上均展现出显著优势，在构建的KITTI3DDC基准上取得了最优性能，验证了该框架在几何信息与语言语义联合建模方面的有效性。本文为Mono3DDC任务提供了全面的基准测试，有效地推动了该任务的发展与实际应用。

Abstract

Monocular 3D object detection provides a low-cost solution for 3D perception. However

existing methods struggle to generate scene descriptions that are intuitively understandable to humans

which limits their applicability in scenarios requiring rich semantic understanding

such as human-computer interaction and autonomous driving. As a direct manifestation of human linguistic intelligence

visual captioning offers an ideal communication medium

endowing machines with the ability to intuitively “narrate” a scene. However

existing visual captioning methods primarily focus on monocular image content and can only describe two-dimensional topological relationships between objects

lacking the capability to accurately model and express 3D geometric information (e.g.

precise distance

spatial location

and motion state). If a two-stage approach is adopted—first performing 3D detection and then leveraging a large model to generate descriptions—it suffers from low system efficiency and poor information consistency. Moreover

the descriptions generated by large models are limited to topological relationships and fail to accurately reflect 3D geometric information. In addition

the inherent “hallucination” phenomenon of large models often leads to inaccurate spatial information accompanied by redundant descriptions. To address these issues

this paper proposes for the first time a novel task: monocular 3D detection and captioning(Mono3DDC). This task aims to unify monocular 3D object detection with caption generation

requiring the model to simultaneously learn depth-aware visual features and linguistic semantics. Through an end-to-end network architecture

the generated descriptions are enabled to accurately capture consistent 3D spatial information

ensuring both geometric accuracy in the descriptions and high precision in 3D object detection. To support in-depth exploration in this interdisciplinary research area

we construct the first benchmark dataset for monocular 3D visual captioning with Chinese semantic support

named KITTI3DDC. Based on the KITTI dataset

this dataset employs an efficient automated data generation pipeline that leverages the synergy between a large language model and structured verification templates. While ensuring diversity and linguistic fluency

the pipeline strictly maintains geometric consistency in spatial information

providing high-quality multimodal supervision signals for subsequent research. Furthermore

we design a novel unified framework

Mono3DDC-TR (Monocular 3D Detection and Captioning based on Transformer). By deeply integrating optimized geometric and visual features

this framework achieves significant advantages in both caption generation accuracy and multi-category 3D detection performance. It attains state-of-the-art results on the constructed KITTI3DDC benchmark

validating the effectiveness of the proposed end-to-end unified framework in jointly modeling geometric information and linguistic semantics. This paper provides a comprehensive benchmark for the Mono3DDC task

effectively promoting its development and practical application.

关键词

Keywords

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