ワンショットニューラルフィールドによる3D物体理解
One-Shot Neural Fields for 3D Object Understanding
単一のRGB画像からNeRFベースの潜在表現を構築し、新規視点レンダリングや3D再構成、把持予測を可能にするロボット向け表現を提案。
著者: Valts Blukis, Taeyeop Lee, Jonathan Tremblay, Bowen Wen, In So Kweon, Kuk-Jin Yoon, Dieter Fox, Stan Birchfield
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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. We expand the NeRF model for additional grasp outputs and explore ways to leverage this representation for robotics. At test-time, we build the representation from a single RGB input image observing the scene from only one viewpoint. We find that the recovered representation allows rendering from novel views, including of occluded object parts, and also for predicting successful stable grasps. Grasp poses can be directly decoded from our latent representation with an implicit grasp decoder. We experimented in both simulation and real world and demonstrated the capability for robust robotic grasping using such compact representation. Website: https://nerfgrasp.github.io
