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arXiv 2401.08045cs.CV

为自动驾驶锻造视觉基础模型：挑战、方法论与机遇

Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities

SSE, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理工学院）；Huawei Zurich Research Center（华为苏黎世研究中心）
Xu Yan, Haiming Zhang, Yingjie Cai, Jingming Guo, Weichao Qiu, Bin Gao, Kaiqiang Zhou, Yue Zhao, Huan Jin, Jiantao Gao, Zhen Li, Lihui Jiang, Wei Zhang, Hongbo … 展开作者

Xu Yan, Haiming Zhang, Yingjie Cai, Jingming Guo, Weichao Qiu, Bin Gao, Kaiqiang Zhou, Yue Zhao, Huan Jin, Jiantao Gao, Zhen Li, Lihui Jiang, Wei Zhang, Hongbo Zhang, Dengxin Dai, Bingbing Liu

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AI总结：

本文系统分析超过250篇论文，剖析为自动驾驶锻造视觉基础模型的数据准备、预训练和任务适配技术，并探讨NeRF、扩散模型等关键进展，提出未来研究路线图。

摘要：

在广泛数据集上训练的大型基础模型的兴起正在革新人工智能领域。SAM、DALL-E2和GPT-4等模型通过提取复杂模式并在多样化任务中高效表现，展示了其适应性，从而成为广泛AI应用的强大构建模块。自动驾驶作为AI应用中一个充满活力的前沿领域，仍然面临缺乏专用视觉基础模型（VFMs）的挑战。全面训练数据的稀缺、多传感器集成的需求以及多样化的任务特定架构，对该领域VFMs的发展构成了重大障碍。本文深入探讨了为自动驾驶量身定制VFMs的关键挑战，同时勾勒了未来方向。通过对超过250篇论文的系统分析，我们剖析了VFM开发的基本技术，包括数据准备、预训练策略和下游任务适配。此外，我们探索了NeRF、扩散模型、3D Gaussian Splatting和世界模型等关键进展，为未来研究呈现了一份全面的路线图。为赋能研究人员，我们构建并维护了https://github.com/zhanghm1995/Forge_VFM4AD，这是一个开放获取的仓库，持续更新自动驾驶VFM锻造的最新进展。

英文摘要：

The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by extracting intricate patterns and performing effectively across diverse tasks, thereby serving as potent building blocks for a wide range of AI applications. Autonomous driving, a vibrant front in AI applications, remains challenged by the lack of dedicated vision foundation models (VFMs). The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs in this field. This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions. Through a systematic analysis of over 250 papers, we dissect essential techniques for VFM development, including data preparation, pre-training strategies, and downstream task adaptation. Moreover, we explore key advancements such as NeRF, diffusion models, 3D Gaussian Splatting, and world models, presenting a comprehensive roadmap for future research. To empower researchers, we have built and maintained https://github.com/zhanghm1995/Forge_VFM4AD, an open-access repository constantly updated with the latest advancements in forging VFMs for autonomous driving.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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