3D Geometric Shape Assembly via Efficient Point Cloud Matching

To this end, we et al., 2023b) to address the task of shape assembly, but these introduce Proxy Match Transform (PMT), an methods fall short of achieving accurate assembly. They approximate high-order feature transform layer typically represent each part as a global embedding and that enables reliable matching between mating perform regression to predict a placement for each part. The surfaces of parts while incurring low costs in global encoding strategy for each part, while simplifying memory and computation. Building upon PMT, the process, greatly limits local information by collapsing we introduce a new framework, dubbed Proxy spatial resolutions, which is necessary to localize the mating Match TransformeR (PMTR), for the geometric surface. Indeed, accurate shape assembly requires a detailed assembly task. We evaluate the proposed PMTR analysis of both fine-and coarse-level spatial information on the large-scale 3D geometric shape assembly of the parts in recognizing mating surfaces and establishing benchmark dataset of Breaking Bad and demonstrate correspondences between the surfaces. Therefore, a its superior performance and efficiency compared promising approach would be to retain the spatially rich to state-of-the-art methods. Project page: part representations during the encoding phase and analyze https://nahyuklee.github.io/pmtr.