Newest papers

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

Om Shivam Verma, Shaya Farahmand, Lingjun Zhao, Lu Gan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28530 · cited by 0 works (OpenAlex) · open-access PDF

Korean

협력적 인식은 에이전트가 개별 에이전트의 시야 영역을 넘어 현장 정의와 기하학을 검색 할 수있게 해줍니다.이 광범위한 현장 이해는 교통 안전을 향상시키고 멀티 로봇 시스템의 효율성을 향상시킵니다. 그러나 기존의 협력적 인 정지적 차지 방법은 밀도가 높은 3D 벡셀 언급에 의존하고 사전 정의 된 정수 클래스 내의 개체를 인식하는 데 제한됩니다. 이러한 제한을 극복하기 위해, 우리는 개방적 인 사전적 Gaussian 기반의 협력적 인 사전적 차지 예측 프레임 워크 인 CoGaussian을 제시합니다. CoGaussian은 기초 모델에 맞춤형 스파르스 3D Gaussians의 세트

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 a set of foundation model-aligned sparse 3D Gaussians, which serve as a compact representation for communicating and fusing geometric and semantic information across multiple agents. On the Semantic-OPV2V benchmark, our method outperforms existing open-vocabulary single-agent approaches, highlighting the effectiveness of open-vocabulary collaborative scene understanding.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Semantics (computer science), Set (abstract data type), Representation (politics), Field (mathematics), Artificial intelligence

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange