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arXiv Computer Vision By Xuying Huang, Swithinraj Moses Daniel, Sicong Pan, Sebastian Houben, Maren Bennewitz

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

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The paper introduces a privacy‑preserving approach for semantic segmentation that fuses high‑resolution depth with ultra‑low‑resolution RGB images. A joint 2D framework uses depth to guide RGB reconstruction and RGB‑D segmentation, while an end‑to‑end 2D‑to‑3D pipeline consolidates 2D features for 3D segmentation. Experiments on ScanNet demonstrate superior 2D and 3D performance compared to other privacy‑preserving methods, strong zero‑shot transfer to SUN RGB‑D and SceneNN, and reduced recoverability of sensitive data, with real‑robot tests showing effective object‑goal navigation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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