Z3D：从图像实现零样本3D视觉定位
Z3D: Zero-Shot 3D Visual Grounding from Images
AI总结：
Z3D通过先进的零样本3D实例分割和基于提示的推理，实现了从多视角图像中无需几何监督的零样本3D视觉定位。
摘要：
3D视觉定位（3DVG）旨在根据自然语言查询在3D场景中定位物体。在本工作中，我们探索仅从多视角图像中实现零样本3DVG，而无需任何几何监督或物体先验知识。我们引入了Z3D，一种通用的定位流程，能够灵活地处理多视角图像，同时可选择性地结合相机姿态和深度图。我们识别出先前零样本方法中的关键瓶颈，导致显著的性能下降，并通过（i）最先进的零样本3D实例分割方法生成高质量的3D边界框提案，以及（ii）基于提示的高级推理，利用现代VLMs的全部能力来解决这些问题。在ScanRefer和Nr3D基准测试中，广泛实验表明，我们的方法在零样本方法中实现了最先进的性能。代码可在https://github.com/col14m/z3d上获得。
英文摘要：
3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. In this work, we explore zero-shot 3DVG from multi-view images alone, without requiring any geometric supervision or object priors. We introduce Z3D, a universal grounding pipeline that flexibly operates on multi-view images while optionally incorporating camera poses and depth maps. We identify key bottlenecks in prior zero-shot methods causing significant performance degradation and address them with (i) a state-of-the-art zero-shot 3D instance segmentation method to generate high-quality 3D bounding box proposals and (ii) advanced reasoning via prompt-based segmentation, which utilizes full capabilities of modern VLMs. Extensive experiments on the ScanRefer and Nr3D benchmarks demonstrate that our approach achieves state-of-the-art performance among zero-shot methods. Code is available at https://github.com/col14m/z3d .
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。
