Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization
Privacy-preserving perception is a critical requirement for deploying 3D scene understanding systems in real-world indoor environments, yet it remains underexplored in open-vocabulary 3D semantic segmentation. Existing methods typically rely on obtaining rich semantic cues from RGB images, which may expose privacy-sensitive visual information. Depth-only 3D geometry provides a privacy-preserving alternative, but the absence of appearance-based semantic cues makes open-vocabulary predictions highly uncertain and less reliable. Under this setting, we propose to convert uncertainty into a guidanc
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- LinkedLinked via arxiv author · 85%Xuying Huang →
“Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization”
- LinkedLinked via arxiv author · 85%Sicong Pan →
“Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization”
- LinkedLinked via arxiv author · 85%Maren Bennewitz →
“Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization”
- FuzzySimilar title/name (fuzzy) · 59%vllm-project/semantic-router →
“Fuzzy title match (0.73): “Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Se” ≈ “vllm-project/semantic-router””
- FuzzySimilar title/name (fuzzy) · 59%microsoft/semantic-kernel →
“Fuzzy title match (0.73): “Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Se” ≈ “microsoft/semantic-kernel””