Segment Any 3D Gaussians

• URL: http://arxiv.org/abs/2312.00860v2
• Date: Mon, 27 May 2024 10:24:31 GMT
• Title: Segment Any 3D Gaussians
• Authors: Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, Qi Tian,
• Abstract summary: This paper presents SAGA, a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS) Given 2D visual prompts as input, SAGA can segment the corresponding 3D target represented by 3D Gaussians within 4 ms. We show that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods.
• Score: 85.93694310363325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents SAGA (Segment Any 3D GAussians), a highly efficient 3D promptable segmentation method based on 3D Gaussian Splatting (3D-GS). Given 2D visual prompts as input, SAGA can segment the corresponding 3D target represented by 3D Gaussians within 4 ms. This is achieved by attaching an scale-gated affinity feature to each 3D Gaussian to endow it a new property towards multi-granularity segmentation. Specifically, a scale-aware contrastive training strategy is proposed for the scale-gated affinity feature learning. It 1) distills the segmentation capability of the Segment Anything Model (SAM) from 2D masks into the affinity features and 2) employs a soft scale gate mechanism to deal with multi-granularity ambiguity in 3D segmentation through adjusting the magnitude of each feature channel according to a specified 3D physical scale. Evaluations demonstrate that SAGA achieves real-time multi-granularity segmentation with quality comparable to state-of-the-art methods. As one of the first methods addressing promptable segmentation in 3D-GS, the simplicity and effectiveness of SAGA pave the way for future advancements in this field. Our code will be released.

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