多模态大模型驱动的三维视觉理解技术前沿进展

冯明涛; 沈军豪; 武子杰; 彭伟星; 钟杭; 郭裕兰; 舒祥波; 张辉; 董伟生; 王耀南

doi:10.11834/jig.240588

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多模态大模型驱动的三维视觉理解技术前沿进展
Recent Progress in Large Multi-modal Model based 3D Vision Understanding
2024年页码：1-47
网络出版日期： 2024-12-30 ，
DOI： 10.11834/jig.240588
稿件说明：

移动端阅览

冯明涛,沈军豪,武子杰等.多模态大模型驱动的三维视觉理解技术前沿进展[J].中国图象图形学报,

Feng Mingtao,Shen Junhao,Wu Zijie,et al.Recent Progress in Large Multi-modal Model based 3D Vision Understanding[J].Journal of Image and Graphics,
冯明涛,沈军豪,武子杰等.多模态大模型驱动的三维视觉理解技术前沿进展[J].中国图象图形学报, DOI： 10.11834/jig.240588.

Feng Mingtao,Shen Junhao,Wu Zijie,et al.Recent Progress in Large Multi-modal Model based 3D Vision Understanding[J].Journal of Image and Graphics, DOI： 10.11834/jig.240588.

摘要

三维（3D）视觉感知和理解在机器人导航、自动驾驶以及智能人机交互等众多领域广泛应用，是计算机视觉领域中备受瞩目的研究方向。随着多模态大模型的发展，它们与3D视觉数据的融合取得了快速进展，为理解和与3D物理世界交互提供了前所未有的能力，并展现了独特优势，如上下文学习、逐步推理、开放词汇能力和丰富的世界知识。本文涵盖了3D视觉数据基本表示，从点云到3D高斯泼溅；梳理了主流多模态大模型的发展脉络；对联合多模态大模型的3D视觉数据表征方法做了详细的归纳总结；梳理了基于多模态大模型的3D理解任务，如3D生成与重建、3D目标检测、3D语义分割、3D场景描述、语言引导的3D目标定位和3D场景问答等；以及基于多模态大模型的机器人具身智能系统中空间理解能力提升；最后梳理了核心数据集和对未来前景的深刻讨论，以期促进该领域的深入研究与广泛应用。本文提出的全面分析揭示了本领域的重大进展，强调了利用多模态大模型进行3D视觉理解的潜力和必要性。因此，本综述目标是为未来的研究绘制一条路线，探索和扩展多模态大模型在理解和与复杂3D世界的互动能力，为空间智能领域的进一步发展铺平道路。

Abstract

Three-dimensional （3D） visual perception and understanding are fundamental to numerous applications， including robotic navigation， autonomous driving， and intelligent human-computer interaction. As one of the most prominent research directions in computer vision， 3D vision has experienced rapid advancement， particularly with the rise of multimodal large models （MLLMs）. The integration of MLLMs with 3D visual data has unlocked unprecedented capabilities for understanding and interacting with the physical 3D world. These models bring distinct advantages， such as contextual learning， step-by-step reasoning， open-vocabulary support， and rich world knowledge， making them transformative tools in the field of 3D vision. This paper provides a comprehensive overview of the latest progress in 3D vision understanding driven by MLLMs. It begins by addressing the foundational representations of 3D visual data. From point clouds to 3D Gaussian splatting， the review systematically examines mainstream data representation methods， which form the backbone for intelligent processing and analysis of 3D visual information. These representations enable the integration of semantic， spatial， and structural information， serving as a critical basis for downstream tasks in 3D vision. Following this， the paper traces the evolution of MLLMs， starting from the development of large language models （LLMs） and their extension into multimodal systems. It highlights the emergence of vision-language models （VLMs） that synergize text and visual data， offering significant potential for advancing 3D vision. The combination of multimodal pre-trained priors and 3D data representations has opened new avenues for cross-modal understanding and interaction. The ability of MLLMs to align multimodal features while reasoning across modalities has proven crucial for overcoming traditional limitations in 3D vision， such as sparse data， occlusion， and noise. The review then focuses on the methods for representing 3D visual data using MLLMs， providing an in-depth synthesis of current strategies. It discusses how MLLMs leverage pre-trained knowledge to interpret 3D information， facilitate multimodal feature alignment， and enhance the contextual understanding of complex 3D scenes. For instance， MLLMs enable the integration of 3D data with semantic priors， improving the efficiency and accuracy of tasks such as 3D object recognition， scene reconstruction， and spatial reasoning. In the context of specific 3D vision tasks， this paper explores various applications of MLLMs， including 3D generation and reconstruction， 3D object detection， semantic segmentation， scene description， language-guided 3D object localization， and 3D scene question answering. These tasks demonstrate the transformative impact of MLLMs on 3D vision， showcasing their ability to elevate task performance by incorporating multimodal capabilities. For example， 3D generation tasks benefit from the contextual knowledge of MLLMs， enabling the creation of semantically coherent and visually accurate 3D content. Similarly， 3D object detection and segmentation tasks leverage the reasoning capabilities of MLLMs to identify and classify objects in complex scenes more effectively. The role of MLLMs extends beyond traditional 3D vision tasks to applications in embodied robotic intelligence systems. Examples include robotic 3D grasping and 3D visual navigation， where MLLMs facilitate spatial understanding and decision-making in dynamic environments. By integrating multimodal reasoning with 3D perception， MLLMs enable robots to perform language-guided manipulations， navigate cluttered spaces， and interact seamlessly with the physical world. The paper also provides a detailed examination of datasets that support research in this domain. It reviews fundamental 3D datasets， such as those designed for point cloud analysis， voxel-based representations， and mesh modeling， alongside multimodal 3D vision-language datasets. These datasets are analyzed in terms of their scale， diversity， and application scope， offering a robust foundation for training and evaluating MLLMs in various 3D vision tasks. The lack of diverse and representative datasets， however， remains a significant challenge， limiting the generalizability and robustness of existing models. Building on these foundations， the paper addresses key challenges and future research priorities in MLLM-driven 3D vision understanding. Major challenges include the unification of 3D representation formats， improving the spatial reasoning capabilities of MLLMs， enhancing the generalization of MLLMs for diverse 3D data processing， and addressing practical deployment mechanisms for multimodal models in real-world 3D vision tasks. Additionally， the paper highlights the need for efficient model miniaturization for edge-side applications， such as autonomous robots and drones， and explores the potential of cloud-edge collaborative frameworks to enhance 3D vision tasks in resource-constrained environments. The future research directions proposed in this paper aim to address these challenges and further advance the field. Key priorities include developing scalable and efficient 3D data representations， designing domain-adaptive MLLMs， and creating comprehensive multimodal benchmarks that reflect real-world complexities. Moreover， the paper emphasizes the importance of fostering innovation in multimodal reasoning frameworks， enabling MLLMs to interpret， generate， and interact with 3D data seamlessly. Exploring the integration of real-time data streams with multimodal pre-training strategies could also provide valuable insights into dynamic 3D environments. By offering a comprehensive analysis of the progress， challenges， and future directions in this field， the paper underscores the transformative potential of MLLMs for 3D vision understanding. It highlights the necessity of leveraging these models to bridge the gap between perception and reasoning， enabling systems to interact effectively with the complex 3D world. The findings emphasize the importance of addressing existing limitations in 3D vision and pave the way for the continued evolution of artificial intelligence in spatially complex and multimodal environments. This review aims to inspire further exploration and expansion of MLLMs in 3D vision understanding， providing a roadmap for future research in this domain. Through the synthesis of state-of-the-art developments， the paper lays a foundation for advancing spatial intelligence， fostering deeper integration between AI systems and the physical world. Ultimately， the analysis highlights the potential of MLLMs to revolutionize 3D vision tasks， promoting their broad application across industries and accelerating the progress of intelligent systems in complex， multimodal scenarios.

关键词

三维视觉多模态大模型三维视觉表征三维视觉生成三维重建机器人三维视觉三维场景理解

Keywords

3D visionLarge multimodal model3d visual representation3D vision generation3D reconstructionRobot 3D vision3D scene understanding

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Yu H， Qin Z， Hou J， Saleh M， Li D， Busam B and ILic S. 2023. Rotation-invariant transformer for point cloud matching //Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 5384-5393 ［DOI： 10.1109/CVPR52729.2023.00521http://dx.doi.org/10.1109/CVPR52729.2023.00521］

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Yu X， Tang L， Rao Y， Huang T， Zhou J and Lu J. 2022. Point-bert： Pre-training 3d point cloud transformers with masked point modeling//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 19291-19300 ［DOI： 10.1109/CVPR52688.2022.01871http://dx.doi.org/10.1109/CVPR52688.2022.01871］

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