LabelAny3D: 在真实世界中标注任意物体的3D
LabelAny3D: Label Any Object 3D in the Wild
AI总结：
LabelAny3D通过分析-合成框架生成高质量3D标注，提升单目3D检测性能，推动真实世界3D识别发展。
摘要：
从单目输入中检测3D空间中的物体对于机器人和场景理解等应用至关重要。尽管在室内和自动驾驶领域表现出色，现有的单目3D检测模型在真实世界图像中面临挑战，因为缺乏真实世界3D数据集以及3D标注的难度。我们介绍了LabelAny3D，一种分析-合成框架，通过从2D图像重建整体3D场景，高效地生成高质量的3D边界框标注。基于该流程，我们提出了COCO3D，一个新的开放词汇单目3D检测基准，源自MS-COCO数据集，涵盖现有3D数据集中未覆盖的广泛物体类别。实验表明，由LabelAny3D生成的标注在多个基准上提升了单目3D检测性能，优于先前的自动标注方法。这些结果展示了基于基础模型的标注在真实、开放世界中扩展3D识别的潜力。
英文摘要：
Detecting objects in 3D space from monocular input is crucial for applications ranging from robotics to scene understanding. Despite advanced performance in the indoor and autonomous driving domains, existing monocular 3D detection models struggle with in-the-wild images due to the lack of 3D in-the-wild datasets and the challenges of 3D annotation. We introduce LabelAny3D, an \emph{analysis-by-synthesis} framework that reconstructs holistic 3D scenes from 2D images to efficiently produce high-quality 3D bounding box annotations. Built on this pipeline, we present COCO3D, a new benchmark for open-vocabulary monocular 3D detection, derived from the MS-COCO dataset and covering a wide range of object categories absent from existing 3D datasets. Experiments show that annotations generated by LabelAny3D improve monocular 3D detection performance across multiple benchmarks, outperforming prior auto-labeling approaches in quality. These results demonstrate the promise of foundation-model-driven annotation for scaling up 3D recognition in realistic, open-world settings.
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