Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds
Year: 2026
Type: conference-paper
Abstract: Volumetric video based on point cloud representations enables immersive virtual and augmented reality applications but introduces significant challenges for efficient and secure content delivery. Prior work proposed a selective coordinate encryption framework for point clouds that encrypts only a subset of coordinates, reducing computational costs while visually degrading unauthorized content. However, it remains unclear whether the remaining unencrypted information is sufficient to enable content reconstruction.
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
Awards: CICI:TCR:CAREFREE:Cloud infrAstructure ResiliencE of the Future foR tEstbeds, accelerators and nEtworks, Collaborative Research: CNS Core: Medium: miVirtualSeat: Semantics-aware Content Distribution for Immersive Meeting Environments, CC* Integration-Large: Prototyping a Secure Distributed Storage Infrastructure for Accelerating Big Science, CC* Integration-Small: Error Free File Transfer for Big Science, CNS Core: Medium: Collaborative Research: Scalable Dissemination and Navigation of Video 360 Content for Personalized Viewing
OpenAlex ID: W7207555542
FWCI:
Cites:
Cited by:
Related to: