We present a unified and compact representation for object rendering, 3D
reconstruction, and grasp pose prediction that can be inferred from a single
image within a few seconds. We achieve this by leveraging recent advances in
the Neural Radiance Field (NeRF) literature that learn category-level priors
and fine-tune on novel objects with minimal data and time. Our insight is that
we can learn a compact shape representation and extract meaningful additional
information from it, such as grasping poses. We believe this to be the first
work to retrieve grasping poses directly from a NeRF-based representation using
a single viewpoint (RGB-only), rather than going through a secondary network
and/or representation. When compared to prior art, our method is two to three
orders of magnitude smaller while achieving comparable performance at view
reconstruction and grasping. Accompanying our method, we also propose a new
dataset of rendered shoes for training a sim-2-real NeRF method with grasping
poses for different widths of grippers.

通过利用神经辐射场（NeRF）的一个紧凑的单一图像表示，我们能够从单一图像中推断物体渲染，三维重建和抓握姿势预测，从而比先前方案小两个到三个数量级，而实现可比的视图重建和抓握性能。