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

TL;DR

This paper introduces ISS-RegAuth, a lightweight indoor space authentication method using sparse ISS keypoints from LiDAR scans, significantly improving speed, privacy, and accuracy for device-independent authentication.

Contribution

It presents a novel keypoint-based approach that reduces data processing and privacy risks while enhancing authentication accuracy in indoor environments.

Findings

Reduces equal-error rate from 0.02 to 0.00

Cuts processing time by 20%

Lowers transmitted data to 2.2% of original

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.