October 3, 2025Open Access

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing

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SGS-3D improves segmentation accuracy and robustness against inaccurate masks, leading to high-fidelity object instances.
Experimental results indicate significant improvements on datasets like ScanNet200 and KITTI-360, showcasing strong generalization.
The framework effectively splits and grows semantic masks, ensuring reliable semantic consistency with 3D object instances.
Combining geometric primitives and semantic information allows for enhanced detection, addressing limitations of traditional methods.

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Implication

The novel SGS-3D framework enhances 3D instance segmentation accuracy in diverse environments, suggesting promising advancements in scene understanding.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68e02f46f0e39f13e7fa2e9c https://doi.org/10.48550/arxiv.2509.05144

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Implication

The novel SGS-3D framework enhances 3D instance segmentation accuracy in diverse environments, suggesting promising advancements in scene understanding.

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing

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Discussion

Implication

Cite This Study

SGS-3D: High-Fidelity 3D Instance Segmentation via Reliable Semantic Mask Splitting and Growing

Key Points

Discussion

Implication

Cite This Study

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