Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications

Xuying Huang; Sicong Pan; Maren Bennewitz

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arXiv · 2607.00978

Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications

Xuying Huang·Sicong Pan·Maren Bennewitz

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Abstract

Privacy-preserving perception is increasingly important for robotic systems operating in real-world indoor environments, yet it remains underexplored in open-vocabulary 3D semantic segmentation. We study this problem under an RGB-prohibited deployment setting motivated by scene-specific visual information disclosure, where real RGB observations are unavailable during scene acquisition and fusion. To reflect this deployment constraint on existing 3D datasets, we adopt a stricter depth-only evaluation protocol that re-runs scene fusion without RGB and exposes only the resulting depth-derived geometry to the segmentation pipeline. This constraint removes appearance cues that are often critical for open-vocabulary recognition, making depth-only predictions more uncertain and less reliable. To address this challenge, we propose UTTO, a model-agnostic uncertainty-guided test-time optimization framework that uses structured predictive uncertainty as a reliability signal to refine predictions from frozen open-vocabulary 3D backbones. Experiments across ScanNet and Matterport3D demonstrate consistent improvements over multiple depth-only backbones. Privacy recoverability analyses and a real-robot semantic goal grounding case study further support the proposed privacy-constrained setting and applicability.

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BibTeX RIS

Xuying Huang, Sicong Pan, Maren Bennewitz. 2026-09-17. Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications. https://arxiv.org/abs/2607.00978

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