Indoor Space Authentication by ISS-based Keypoint Extraction from 3D Point Clouds

2026-03-06

AI Summary

This work proposes ISS-RegAuth, a novel framework for indoor spatial authentication that addresses the limitations of existing methods, which rely on dense point clouds and are thus susceptible to interference from planar structures such as walls and floors, leading to high computational latency and privacy risks. By leveraging sparse representations based on Intrinsic Shape Signatures (ISS) keypoints—utilizing only 1–2% of the original points—combined with FPFH features, RANSAC, and ICP algorithms, the approach achieves highly efficient registration. Evaluated on the ARKitScenes dataset, ISS-RegAuth reduces the equal error rate to 0.00, decreases processing time by 20%, and lowers transmitted data volume to just 2.2% of the original, significantly enhancing both authentication efficiency and privacy preservation while advancing device-agnostic identity verification mechanisms.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Computer Vision: Motion & Tracking

Application Category

• Security and Privacy: Authentication, authorization, and access control
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We propose ISS-RegAuth, a lightweight indoor space authentication framework that authenticates a user by comparing LiDAR captures of personal rooms. Prior work processes every point in the cloud, where planar surfaces such as walls and floors dominate similarity calculations, causing latency and potential privacy exposure. In contrast, ISS-RegAuth retains only 1-2\% of Intrinsic Shape Signatures (ISS) keypoints, computes their Fast Point Feature Histograms, and performs RANSAC and ICP on this sparse set. On 100 ARKitScenes pairs, this approach reduces the equal-error rate from 0.02 to 0.00, cuts processing time by 20\%, and lowers transmitted data to 2.2\% of the original. These results show that keypoint-based sparse representation can make privacy-preserving, edge-deployable indoor space authentication practical. As an early step, this work opens a path toward device-independent authentication and account-recovery mechanisms that rely on users'physical environments.

Problem

• indoor space authentication
• 3D point clouds
• privacy-preserving
• edge computing
• LiDAR

Innovation

• ISS keypoints
• 3D point cloud
• indoor space authentication
• privacy-preserving
• edge-deployable