One-Shot Neural Fields for 3D Object Understanding

Understand

We present a unified and compact scene representation for robotics, where each object in the scene is depicted by a latent code capturing geometry and appearance.

• This representation can be decoded for various tasks such as novel view rendering, 3D reconstruction (e.g.
• recovering depth, point clouds, or voxel maps), collision checking, and stable grasp prediction.
• We build our representation from a single RGB input image at test time by leveraging recent advances in Neural Radiance Fields (NeRF) that learn category-level priors on large multiview datasets, then fine-tune on novel objects from one or few views.