Segment then Splat: A Unified Approach for 3D Open-Vocabulary Segmentation based on Gaussian Splatting

• URL: http://arxiv.org/abs/2503.22204v1
• Date: Fri, 28 Mar 2025 07:36:51 GMT
• Title: Segment then Splat: A Unified Approach for 3D Open-Vocabulary Segmentation based on Gaussian Splatting
• Authors: Yiren Lu, Yunlai Zhou, Yiran Qiao, Chaoda Song, Tuo Liang, Jing Ma, Yu Yin,
• Abstract summary: Open-vocabulary querying in 3D space is crucial for enabling more intelligent perception in applications such as robotics, autonomous systems, and augmented reality.<n>Most existing methods rely on 2D pixel-level parsing, leading to multi-view inconsistencies and poor 3D object retrieval.<n>We propose Segment then, a 3D-aware open vocabulary segmentation approach for both static and dynamic scenes.
• Score: 11.186317340623807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open-vocabulary querying in 3D space is crucial for enabling more intelligent perception in applications such as robotics, autonomous systems, and augmented reality. However, most existing methods rely on 2D pixel-level parsing, leading to multi-view inconsistencies and poor 3D object retrieval. Moreover, they are limited to static scenes and struggle with dynamic scenes due to the complexities of motion modeling. In this paper, we propose Segment then Splat, a 3D-aware open vocabulary segmentation approach for both static and dynamic scenes based on Gaussian Splatting. Segment then Splat reverses the long established approach of "segmentation after reconstruction" by dividing Gaussians into distinct object sets before reconstruction. Once the reconstruction is complete, the scene is naturally segmented into individual objects, achieving true 3D segmentation. This approach not only eliminates Gaussian-object misalignment issues in dynamic scenes but also accelerates the optimization process, as it eliminates the need for learning a separate language field. After optimization, a CLIP embedding is assigned to each object to enable open-vocabulary querying. Extensive experiments on various datasets demonstrate the effectiveness of our proposed method in both static and dynamic scenarios.

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