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This paper introduces InViStream, a real-time privacy-preserving system for volumetric video streaming that addresses the unique challenges of multi-view RGB-D data. By integrating object detection with depth-aware masking and ensuring consistent separation of public and private instances, InViStream effectively prevents sensitive content from being reconstructed in shared 3D representations. The system achieves impressive performance metrics, including a synthetic Dice score of 0.799 and real-time streaming capabilities exceeding 30 FPS, making it suitable for applications in telepresence and immersive environments.
InViStream effectively eliminates the risk of private content reappearing in 3D reconstructions, achieving high accuracy while maintaining real-time performance.
Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame by frame, RGB-D volumetric pipelines capture people, rooms, and personal objects from multiple cameras and fuse them into a shared 3D representation. A private object missed in one view, or only partially removed before fusion, can therefore reappear in the reconstructed scene. This creates a privacy challenge for 3D telepresence, education, entertainment, and immersive applications: private content should be removed before raw visual and geometric data leave the camera side, while the public part of the scene should remain useful for real-time reconstruction. Existing volumetric streaming systems mainly optimize reconstruction, data movement, and latency, while privacy-preserving vision methods are designed for single-camera, single-frame images and do not directly address calibrated multi-view RGB-D fusion. We present InViStream, a real-time"privacy-from-source"system designed for this setting. InViStream addresses three challenges in volumetric capture: private objects may appear differently across views, RGB masking alone can leave geometric privacy leakage in depth, and public/private instances of the same class must be separated consistently before cloud-side fusion. To address these challenges, InViStream combines object detection with depth-aware masking, propagates public/private decisions across calibrated views, and fuses only sanitized point clouds. We evaluate InViStream on synthetic and real RGB-D scenes, including offices, conference rooms, living rooms, and settings with multiple public and private people and objects. InViStream achieves synthetic Dice/Recall of 0.799/0.891 and real Dice/Recall of 0.792/0.908, with synthetic SSIM above 0.98 and real-time streaming above 30 FPS.