Robust multi-view PPF-based method for multi-instance pose estimation

Abstract Multi-instance 6D pose estimation is a fundamental task in processing depth images and point cloud data for industrial robots and automation. This task is often hindered by challenges such as a high number of pseudo outliers, instance occlusions, and low overlap between instances and models. The Point Pair Feature (PPF) is a widely recognized concept for addressing multi-instance pose estimation, characterized by its lack of training requirements, robustness to occlusion, and exceptional ease of use. However, existing PPF-based methods exhibit relatively poor performance, particularly when compared to machine learning-based approaches. In this paper, we propose a robust multi-view PPF-based method that specifically addresses the challenges of generating multi-view models and enhancing model generalization. Additionally, we introduce a comprehensive usage framework for multi-view models. This framework incorporates background removal and scene segmentation for preprocessing, a multi-view PPF-based approach for primary computation, and a multi-instance spatial structure to eliminate erroneous results during post-processing. When evaluated on the ITODD datasets from the BOP Challenge, our method achieves the SOTA performance among traditional methods for 3D point cloud data, with an average recall of 69.6%. These results demonstrate the following: (1) a significant performance improvement of 44.7% compared to the leading conventional method, and (2) performance that is nearly equivalent to that of the leading machine learning method. These results underscore the robustness and effectiveness of our method in advancing multi-instance pose estimation for industrial applications.