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

As mobile robots become increasingly integrated into everyday environments, privacy risks arising from onboard cameras have become a growing concern. Ultra-low-resolution ({ULR}) {RGB} can mitigate visual privacy exposure at the source, but {ULR} appearance alone substantially limits semantic and spatial understanding. We therefore introduce a privacy-preserving asymmetric sensing setting that combines high-resolution ({HR}) depth with {ULR} {RGB}, preserving dense geometry while restricting fine-grained visual information. To address the severe information imbalance between {HR} depth and {ULR} {RGB}, we propose a joint 2D framework using {HR} geometry to guide semantic-oriented {RGB} reconstruction and {RGB}-D segmentation. Despite reliable frame-level predictions, consistent scene-level understanding remains challenging under the asymmetric {HR} depth–{ULR} {RGB} setting. We therefore develop an end-to-end 2D-to-3D pipeline that consolidates 2D semantic features for 3D segmentation. Experiments on {ScanNet} show that our method achieves the best 2D and 3D segmentation performance among privacy-preserving approaches and delivers the strongest zero-shot transfer to {SUN} {RGB}-D and {SceneNN}. Privacy recoverability analysis shows that our proposed {HR} depth–{ULR} {RGB} input reduces the recoverability of sensitive data, and real-robot experiments demonstrate the utility of the resulting 3D semantics for object-goal navigation.