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.

翻译：高精度三维物体位姿估计是实现涉及复杂物体交互的机器人应用的关键。本研究证明，采用最先进的高效辐射场重建方法生成的密度场，能够对已知三维模型的物体实现高度精确且鲁棒的位姿估计——即使物体尺寸极小且表面具有强反射特性。我们提出了一种基于腕装单目相机与机器人臂的完全自动化物体位姿估计系统，可在数分钟内从零开始扫描场景，检测并估计多个物体的六自由度（6-DoF）位姿。对于螺栓、螺母等微小物体，其位姿估计精度可达毫米级。