Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognition

TL;DR

This paper introduces a robust 3D object recognition method that leverages rotational subgroup voting and pose clustering, effectively handling noise, outliers, and occlusions in point cloud data.

Contribution

The authors propose a novel voting scheme based on rotational subgroups and kernel density estimation for stable 6 DoF pose estimation in noisy and cluttered environments.

Findings

Handles high outlier rates better than RANSAC

Achieves perfect recall on two LIDAR datasets

Outperforms existing methods on RGB-D datasets

Abstract

It is possible to associate a highly constrained subset of relative 6 DoF poses between two 3D shapes, as long as the local surface orientation, the normal vector, is available at every surface point. Local shape features can be used to find putative point correspondences between the models due to their ability to handle noisy and incomplete data. However, this correspondence set is usually contaminated by outliers in practical scenarios, which has led to many past contributions based on robust detectors such as the Hough transform or RANSAC. The key insight of our work is that a single correspondence between oriented points on the two models is constrained to cast votes in a 1 DoF rotational subgroup of the full group of poses, SE(3). Kernel density estimation allows combining the set of votes efficiently to determine a full 6 DoF candidate pose between the models. This modal pose with the highest density is stable under challenging conditions, such as noise, clutter, and occlusions, and provides the output estimate of our method. We first analyze the robustness of our method in relation to noise and show that it handles high outlier rates much better than RANSAC for the task of 6 DoF pose estimation. We then apply our method to four state of the art data sets for 3D object recognition that contain occluded and cluttered scenes. Our method achieves perfect recall on two LIDAR data sets and outperforms competing methods on two RGB-D data sets, thus setting a new standard for general 3D object recognition using point cloud data.