Indoor Space Authentication by ISS-based Keypoint Extraction from 3D Point Clouds
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914374609797120 |
|---|---|
| author | Yamada, Yuki Kotani, Daisuke Tsubouchi, Kota Gomi, Hidehito Okabe, Yasuo |
| author_facet | Yamada, Yuki Kotani, Daisuke Tsubouchi, Kota Gomi, Hidehito Okabe, Yasuo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_05858 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Indoor Space Authentication by ISS-based Keypoint Extraction from 3D Point Clouds Yamada, Yuki Kotani, Daisuke Tsubouchi, Kota Gomi, Hidehito Okabe, Yasuo Cryptography and Security 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. |
| title | Indoor Space Authentication by ISS-based Keypoint Extraction from 3D Point Clouds |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2603.05858 |