Newest papers

CoGaussian: Open-Vocabulary Gaussian-based Collaborative 3D Scene Understanding

Om Shivam Verma, Shaya Farahmand, Lingjun Zhao, Lu Gan · 2026 · arXiv

DOI: 10.48550/arXiv.2610.28530 · open-access PDF

Chinese

协作感知使代理人能够在任何个体代理人视野之外检索场景的语义和几何。这种更广泛的场景理解增强了运输安全性和提高了多机器人系统的效率。然而,现有的协作语义占用方法依赖于密集的3D语音符号注释,并且仅限于在预定义的语义类别中识别对象。 为了克服这些局限性,我们介绍了CoGaussian,一个基于开源的Gaussian的协作语义占用预测框架。

Machine translation (Meta M2M100); the English original follows.

Abstract

Collaborative perception enables agents to retrieve the scene semantics and geometry beyond the field of view of any individual agent. This broader scene understanding enhances transportation safety and improves efficiency in multi-robot systems. However, existing collaborative semantic occupancy methods rely on dense 3D voxel annotations and are limited to recognizing objects within a predefined set of semantic classes. To overcome these limitations, we present CoGaussian, an open-vocabulary Gaussian-based collaborative semantic occupancy prediction framework. CoGaussian represents a scene as

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