3D Geometric Shape Assembly via Efficient Point Cloud Matching
<|reference_start|>3D Geometric Shape Assembly via Efficient Point Cloud Matching: 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.<|reference_end|>
@article{lee20243d,
    title={3D Geometric Shape Assembly via Efficient Point Cloud Matching},
    author={Nahyuk Lee, Juhong Min, Junha Lee, Seungwook Kim, Kanghee Lee, Jaesik
  Park, Minsu Cho},
    journal={arXiv preprint arXiv:2407.10542},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10542},
    primaryClass={cs.CV cs.AI}
}