Fit-NGP: Fitting Object Models to Neural Graphics Primitives
Marwan Taher Ignacio Alzugaray Andrew J. Davison
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.