Privacy-Preserving Semantic Segmentation from High-Resolution Depth and Ultra-Low-Resolution RGB

arXiv:2609.28360 · cs.CV, cs.RO · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.RO

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: Xuying Huang and Swithinraj Moses Daniel have equal contribution

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Computer Vision and Pattern Recognition — 9× in this paper
• Generalization — 9× in this paper · explained in 13 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Zero-Shot Generalization — 7× in this paper · explained in 5 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Aggregation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Consistency — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Deformable Attention Transformer — 1× in this paper
• Domain Shift — 1× in this paper · explained in 4 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Inspired by Spatial Feature Transform — 1× in this paper
• Point Clouds — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Depth-Only Open-Vocabulary 3D Semantic Segmentation For Privacy-Preserving Robotic Applications
• Privacy-Preserving Semantic Segmentation from Ultra-Low-Resolution RGB Inputs
• The Need for Inherently Privacy-Preserving Vision in Trustworthy Autonomous Systems
• PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks

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