3D Geometric Shape Assembly via Efficient Point Cloud Matching

Autor:	Lee, Nahyuk, Min, Juhong, Lee, Junha, Kim, Seungwook, Lee, Kanghee, Park, Jaesik, Cho, Minsu
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to ICML 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.10542 Zobrazit plný text záznamu View this record from Arxiv