Fit-NGP: Fitting Object Models to Neural Graphics Primitives

• URL: http://arxiv.org/abs/2401.02357v1
• Date: Thu, 4 Jan 2024 16:57:56 GMT
• Title: Fit-NGP: Fitting Object Models to Neural Graphics Primitives
• Authors: Marwan Taher, Ignacio Alzugaray, Andrew J. Davison
• Abstract summary: We show that the density field created by a state-of-the-art efficient radiance field reconstruction method is suitable for highly accurate pose estimation. We present a fully automatic object pose estimation system based on a robot arm with a single wrist-mounted camera.
• Score: 19.513102875891775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate 3D object pose estimation is key to enabling many robotic applications that involve challenging object interactions. In this work, we show that the density field created by a state-of-the-art efficient radiance field reconstruction method is suitable for highly accurate and robust pose estimation for objects with known 3D models, even when they are very small and with challenging reflective surfaces. We present a fully automatic object pose estimation system based on a robot arm with a single wrist-mounted camera, which can scan a scene from scratch, detect and estimate the 6-Degrees of Freedom (DoF) poses of multiple objects within a couple of minutes of operation. Small objects such as bolts and nuts are estimated with accuracy on order of 1mm.

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