OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

• URL: http://arxiv.org/abs/2503.01646v1
• Date: Mon, 03 Mar 2025 15:23:21 GMT
• Title: OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding
• Authors: Dianyi Yang, Yu Gao, Xihan Wang, Yufeng Yue, Yi Yang, Mengyin Fu,
• Abstract summary: OpenGS-SLAM is an innovative framework that utilizes 3D Gaussian representation to perform dense semantic SLAM in open-set environments.<n>Our system integrates explicit semantic labels derived from 2D models into the 3D Gaussian framework, facilitating robust 3D object-level understanding.<n>Our method achieves 10 times faster semantic rendering and 2 times lower storage costs compared to existing methods.
• Score: 20.578106363482018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in 3D Gaussian Splatting have significantly improved the efficiency and quality of dense semantic SLAM. However, previous methods are generally constrained by limited-category pre-trained classifiers and implicit semantic representation, which hinder their performance in open-set scenarios and restrict 3D object-level scene understanding. To address these issues, we propose OpenGS-SLAM, an innovative framework that utilizes 3D Gaussian representation to perform dense semantic SLAM in open-set environments. Our system integrates explicit semantic labels derived from 2D foundational models into the 3D Gaussian framework, facilitating robust 3D object-level scene understanding. We introduce Gaussian Voting Splatting to enable fast 2D label map rendering and scene updating. Additionally, we propose a Confidence-based 2D Label Consensus method to ensure consistent labeling across multiple views. Furthermore, we employ a Segmentation Counter Pruning strategy to improve the accuracy of semantic scene representation. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method in scene understanding, tracking, and mapping, achieving 10 times faster semantic rendering and 2 times lower storage costs compared to existing methods. Project page: https://young-bit.github.io/opengs-github.github.io/.

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