MPF6D: Masked Pyramid Fusion 6D Pose Estimation

TL;DR

MPF6D introduces a real-time 6D object pose estimation method using RGB-D data, leveraging a pyramid neural network architecture for improved accuracy over existing approaches.

Contribution

The paper proposes a novel neural network architecture with multiple heads and pyramid feature fusion for enhanced 6D pose estimation from RGB-D data.

Findings

Achieves higher accuracy than state-of-the-art methods

Operates in real-time with low inference time

Validated on two common datasets

Abstract

Object pose estimation has multiple important applications, such as robotic grasping and augmented reality. We present a new method to estimate the 6D pose of objects that improves upon the accuracy of current proposals and can still be used in real-time. Our method uses RGB-D data as input to segment objects and estimate their pose. It uses a neural network with multiple heads to identify the objects in the scene, generate the appropriate masks and estimate the values of the translation vectors and the quaternion that represents the objects' rotation. These heads leverage a pyramid architecture used during feature extraction and feature fusion. We conduct an empirical evaluation using the two most common datasets in the area, and compare against state-of-the-art approaches, illustrating the capabilities of MPF6D. Our method can be used in real-time with its low inference time and high accuracy.