综述视觉语言模型在3D目标检测中的应用与挑战
A Review of 3D Object Detection with Vision-Language Models
- 对比传统点云方法与CLIP等多模态框架的差异
- 提出开放词汇检测与零样本泛化能力提升方案
- 适合关注3D感知与多模态融合的研究者
本文系统分析了超过100篇论文，首次全面梳理了视觉语言模型（VLMs）在3D目标检测中的研究进展。相较于2D检测，3D检测面临空间推理复杂、数据结构多样等挑战。传统基于点云和体素网格的方法逐步被以CLIP和3D大语言模型为代表的现代多模态框架替代，实现了开放词汇检测与零样本泛化。文章回顾了关键架构、预训练策略及提示工程方法，探讨了文本与3D特征对齐机制。通过可视化示例与基准评测，揭示模型性能与行为特征。最后指出当前局限，如3D-语言数据集稀缺、计算成本高，并提出未来发展方向。
原文摘要 · Abstract (English)
This review provides a systematic analysis of comprehensive survey of 3D object detection with vision-language models(VLMs) , a rapidly advancing area at the intersection of 3D vision and multimodal AI. By examining over 100 research papers, we provide the first systematic analysis dedicated to 3D object detection with vision-language models. We begin by outlining the unique challenges of 3D object detection with vision-language models, emphasizing differences from 2D detection in spatial reasoning and data complexity. Traditional approaches using point clouds and voxel grids are compared to modern vision-language frameworks like CLIP and 3D LLMs, which enable open-vocabulary detection and zero-shot generalization. We review key architectures, pretraining strategies, and prompt engineering methods that align textual and 3D features for effective 3D object detection with vision-language models. Visualization examples and evaluation benchmarks are discussed to illustrate performance and behavior. Finally, we highlight current challenges, such as limited 3D-language datasets and computational demands, and propose future research directions to advance 3D object detection with vision-language models. >Object Detection, Vision-Language Models, Agents, VLMs, LLMs, AI
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