3D Geometric Shape Assembly via Efficient Point Cloud Matching
This work addresses the task of 3D shape assembly, which is important for applications in robotics and manufacturing, but it appears incremental as it builds on existing matching techniques with a new framework.
The paper tackles the problem of assembling 3D geometric shapes into larger structures by establishing local correspondences between point clouds, achieving superior performance and efficiency on the Breaking Bad benchmark dataset compared to state-of-the-art methods.
Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and computation. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://nahyuklee.github.io/pmtr.