Deep learning methods for perception are the cornerstone of many robotic
systems. Despite their potential for impressive performance, obtaining
real-world training data is expensive, and can be impractically difficult for
some tasks. Sim-to-real transfer with domain randomization offers a potential
workaround, but often requires extensive manual tuning and results in models
that are brittle to distribution shift between sim and real. In this work, we
introduce Composable Object Volume NeRF (COV-NeRF), an object-composable NeRF
model that is the centerpiece of a real-to-sim pipeline for synthesizing
training data targeted to scenes and objects from the real world. COV-NeRF
extracts objects from real images and composes them into new scenes, generating
photorealistic renderings and many types of 2D and 3D supervision, including
depth maps, segmentation masks, and meshes. We show that COV-NeRF matches the
rendering quality of modern NeRF methods, and can be used to rapidly close the
sim-to-real gap across a variety of perceptual modalities.

COV-NeRF 是一个用于合成真实世界训练数据的 NeRF 模型，通过从真实图像中提取对象并将其组合到新场景中，生成逼真的渲染图像和多种 2D 和 3D 监督信息，包括深度图、分割掩膜和网格，以快速消除各种感知模式之间的模拟与真实差距。