Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs
Nikhil Mishra Maximilian Sieb Pieter Abbeel Xi Chen
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.