works on

From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.CV2026

Geometry-Driven Opti-Acoustic Co-Registration and View-Invariant Reflectivity Mapping for Side-Scan Sonar

Taqi Hamoda, Nuno Gracias

Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping, yet automated perception and cross-modal alignment are severely bottlenecked by acoustic complexitie…

cs.CV2026

BenthicDINO: Physics-Informed Self-Distillation for View-Invariant Side-Scan Sonar Representations

Taqi Hamoda, Hayat Rajani, Nuno Gracias

Automated perception in side-scan sonar (SSS) imagery is severely hindered by physical acoustic artifacts, resulting in representations that inextricably mix intrinsic seabed refle…

physics.ao-ph2026

SC-Match: Scale-Space Matching with Context Consistency for Side-Scan Sonar Mapping

Can Lei, Rafael Garcia, Nuno Gracias +2

Reliable estimation of spatial correspondences between overlapping side-scan sonar (SSS) measurements is essential for mapping, but acoustic appearance variations, weak seabed text…

cs.CV2026

BenthiCat: An opti-acoustic dataset for advancing benthic classification and habitat mapping

Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles +3

The paper presents BenthiCat, a large multi‑modal dataset of side‑scan sonar tiles, bathymetric maps, and co‑registered optical images for training and benchmarking machine learnin…

physics.ao-ph2025

PhysDNet: Physics-Guided Decomposition Network of Side-Scan Sonar Imagery

Can Lei, Hayat Rajani, Nuno Gracias +2

Side-scan sonar (SSS) imagery is widely used for seafloor mapping and underwater remote sensing, yet the measured intensity is strongly influenced by seabed reflectivity, terrain e…

physics.ins-det2025

A Geometrically Consistent Matching Framework for Side-Scan Sonar Mapping

Can Lei, Hayat Rajani, Nuno Gracias +2

Robust matching of side-scan sonar imagery remains a fundamental challenge in seafloor mapping due to view-dependent backscatter, shadows, and geometric distortion. This paper prop…