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

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.

Accepted for International Conference on Computer Vision (ICCV), 2017

References in corpus (4)

• Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
• Learning Descriptors for Object Recognition and 3D Pose Estimation
• Going Further with Point Pair Features
• Deep Learning of Local RGB-D Patches for 3D Object Detection and 6D Pose Estimation

Cited by in corpus (12)

• DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
• Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands
• BOP: Benchmark for 6D Object Pose Estimation
• 3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review
• L3DOC: Lifelong 3D Object Classification
• P${}^2$GNet: Pose-Guided Point Cloud Generating Networks for 6-DoF Object Pose Estimation
• 3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation
• I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
• Performance comparison of 3D correspondence grouping algorithm for 3D plant point clouds
• A Summary of the 4th International Workshop on Recovering 6D Object Pose
• A Dynamic Keypoints Selection Network for 6DoF Pose Estimation
• Physics-based Scene-level Reasoning for Object Pose Estimation in Clutter