HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering

• URL: http://arxiv.org/abs/2504.13590v1
• Date: Fri, 18 Apr 2025 09:48:42 GMT
• Title: HAECcity: Open-Vocabulary Scene Understanding of City-Scale Point Clouds with Superpoint Graph Clustering
• Authors: Alexander Rusnak, Frédéric Kaplan,
• Abstract summary: We present Hierarchical vocab-Agnostic Expert Clustering (HAEC), after the latin word for 'these'<n>We administer this highly scalable approach to the first application of open-vocabulary scene understanding on the SensatUrban city-scale dataset.<n>Our technique can help unlock complex operations on dense urban 3D scenes and open a new path forward in the processing of digital twins.
• Score: 49.64902130083662
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional 3D scene understanding techniques are generally predicated on hand-annotated label sets, but in recent years a new class of open-vocabulary 3D scene understanding techniques has emerged. Despite the success of this paradigm on small scenes, existing approaches cannot scale efficiently to city-scale 3D datasets. In this paper, we present Hierarchical vocab-Agnostic Expert Clustering (HAEC), after the latin word for 'these', a superpoint graph clustering based approach which utilizes a novel mixture of experts graph transformer for its backbone. We administer this highly scalable approach to the first application of open-vocabulary scene understanding on the SensatUrban city-scale dataset. We also demonstrate a synthetic labeling pipeline which is derived entirely from the raw point clouds with no hand-annotation. Our technique can help unlock complex operations on dense urban 3D scenes and open a new path forward in the processing of digital twins.

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