用神经辐射场自动构建高质量3D数据集，解决标注难、成本高的问题。
Automating 3D Dataset Generation with Neural Radiance Fields
- 利用神经辐射场生成任意物体的高精度3D模型
- 训练的检测网络在典型场景中表现优异
- 流程自动化程度高，适合快速构建新类别数据集
3D检测是理解环境空间特征的关键任务，广泛应用于机器人、增强现实和图像检索等领域。训练高性能检测模型需要大量多样、精确标注且规模庞大的数据集，但现有公开数据集数量稀少，类别覆盖有限，且创建过程复杂昂贵。本文提出一种针对任意物体的3D数据集自动生成流水线。利用神经辐射场（Neural Radiance Fields）的通用3D表示与渲染能力，该流水线可生成高质量3D模型，并作为合成数据集生成器的输入。整个流程快速、易用、高度自动化。实验表明，使用本方法生成的数据集训练的3D姿态估计网络，在典型应用场景中表现出色。
原文摘要 · Abstract (English)
3D detection is a critical task to understand spatial characteristics of the environment and is used in a variety of applications including robotics, augmented reality, and image retrieval. Training performant detection models require diverse, precisely annotated, and large scale datasets that involve complex and expensive creation processes. Hence, there are only few public 3D datasets that are additionally limited in their range of classes. In this work, we propose a pipeline for automatic generation of 3D datasets for arbitrary objects. By utilizing the universal 3D representation and rendering capabilities of Radiance Fields, our pipeline generates high quality 3D models for arbitrary objects. These 3D models serve as input for a synthetic dataset generator. Our pipeline is fast, easy to use and has a high degree of automation. Our experiments demonstrate, that 3D pose estimation networks, trained with our generated datasets, archive strong performance in typical application scenarios.
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