MObI：基于扩散模型的多模态对象修复
MObI: Multimodal Object Inpainting Using Diffusion Models
AI总结：
MObI利用扩散模型和3D边界框条件，实现相机与激光雷达多模态场景中对象的真实可控插入，提升感知模型测试的灵活性与真实性。
摘要：
安全关键应用（如自动驾驶）需要大量多模态数据进行严格测试。由于收集真实世界数据的成本和复杂性，基于合成数据的方法日益受到重视，但此类方法需要高度真实感和可控性才能发挥作用。本文介绍了MObI，一种新颖的多模态对象修复框架，利用扩散模型在多种感知模态（同时针对相机和激光雷达进行演示）中生成真实且可控的对象修复。仅使用单个参考RGB图像，MObI即可将对象无缝插入现有多模态场景中由边界框指定的3D位置，同时保持语义一致性和多模态连贯性。与传统仅依赖编辑掩码的修复方法不同，我们的3D边界框条件赋予对象精确的空间定位和真实的缩放。因此，我们的方法可灵活地将新对象插入多模态场景，为测试感知模型提供显著优势。
英文摘要：
Safety-critical applications, such as autonomous driving, require extensive multimodal data for rigorous testing. Methods based on synthetic data are gaining prominence due to the cost and complexity of gathering real-world data but require a high degree of realism and controllability in order to be useful. This paper introduces MObI, a novel framework for Multimodal Object Inpainting that leverages a diffusion model to create realistic and controllable object inpaintings across perceptual modalities, demonstrated for both camera and lidar simultaneously. Using a single reference RGB image, MObI enables objects to be seamlessly inserted into existing multimodal scenes at a 3D location specified by a bounding box, while maintaining semantic consistency and multimodal coherence. Unlike traditional inpainting methods that rely solely on edit masks, our 3D bounding box conditioning gives objects accurate spatial positioning and realistic scaling. As a result, our approach can be used to insert novel objects flexibly into multimodal scenes, providing significant advantages for testing perception models.
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。
