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

• URL: http://arxiv.org/abs/2608.23479v1
• Date: Mon, 24 Aug 2026 16:44:46 GMT
• Title: Geometry-Driven Opti-Acoustic Co-Registration and View-Invariant Reflectivity Mapping for Side-Scan Sonar
• Abstract summary: Side-Scan Sonar (SSS) is a primary modality for large-scale underwater mapping.<n>Traditional descriptors fail to bridge the physical domain gap between optical and acoustic imagery.<n>We propose a novel framework for pixel-level opti-acoustic co-registration and view-invariant reflectivity mapping.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 complexities such as speckle noise, shadows, and extreme viewpoint dependencies. Traditional handcrafted descriptors and modern deep learning matchers fail to bridge the physical domain gap between optical and acoustic imagery without 3D geometric constraints. To overcome these limitations, we propose a novel geometry-driven framework for pixel-level opti-acoustic co-registration and view-invariant reflectivity mapping. Our method utilizes Structure-from-Motion (SfM) to reconstruct a dense 3D seafloor mesh, acting as a geometric anchor between the visual and acoustic domains. We introduce a First Bottom Return (FBR) extraction algorithm to dynamically correct non-linear altitude drift caused by uncalibrated SfM reconstruction. Furthermore, we apply an inverse Lambertian model and a dual-Gaussian weighting function to isolate the intrinsic seabed reflectivity, effectively neutralizing slant-range propagation loss and geometric view-dependence. By deterministically associating these isolated acoustic properties with optical pixels, our pipeline generates highly accurate, strictly co-registered multi-modal datasets. This automated, physics-guided approach eliminates the need for manual annotation and paves the way for advanced self-supervised learning in benthic habitat mapping.

Related papers

• Dynamic-Robust Photometric-Semantic Reconstruction for Open-Vocabulary 3D Scene Understanding [63.599342750566485]
  SPAR is a novel joint semantic-geometric encoding architecture that explicitly isolates transient dynamic noise prior to latent space aggregation.<n>Our end-to-end approach achieves exceptional novel view synthesis quality, yielding a PSNR of 22.15 dB and 23.33 dB given only 3 and 4 input views respectively.
  arXiv  Detail & Related papers  (2026-08-29T09:58:24Z)
• Visual Relocalization from Sparse Views in Aliased and Low-Texture Environments via Novel View Synthesis [17.82023714307905]
  We propose a visual relocalization method that departs from classical correspondence-based pipelines.<n>Our key contribution is a geometry-aware training strategy that combines photometric and geometric losses.<n>We show that this joint optimization produces a 3DGS model that better fits the underlying scene geometry.
  arXiv  Detail & Related papers  (2026-07-24T09:46:07Z)
• R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting [59.29134814041703]
  Physical adversarial camouflage poses a severe security threat to autonomous driving systems.<n>Current methods fail to generalize across diverse geometric (e.g., viewing configurations) and radiometric variations.<n>We propose the Relightable Physical 3D Gaussian Splatting framework (R-PGA)
  arXiv  Detail & Related papers  (2026-03-27T04:38:04Z)
• PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction [76.9760302941819]
  PolGS++ is a polarimetric Gaussian Splatting framework for fast reflective surface reconstruction.<n>We introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints.
  arXiv  Detail & Related papers  (2026-03-11T14:11:42Z)
• Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing [59.12032628787018]
  3D mesh saliency ground truth is essential for human-centric visual modeling in virtual reality (VR)<n>Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps.<n>This paper proposes a robust framework to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T05:20:12Z)
• EndoWave: Rational-Wavelet 4D Gaussian Splatting for Endoscopic Reconstruction [18.43808203690038]
  endoscopic scenarios present unique challenges, including photometric inconsistencies, non-rigid tissue motion, and view-dependent highlights.<n>Most 3DGS-based methods rely that solely on appearance constraints for optimizing 3DGS are often insufficient in this context.<n>We present EndoWave, which incorporates an optical flow-based geometric constraint and a multi-resolution rational wavelet supervision.
  arXiv  Detail & Related papers  (2025-10-27T07:45:17Z)
• Non-Rigid Structure-from-Motion via Differential Geometry with Recoverable Conformal Scale [17.935227965480475]
  We introduce a novel method, called Con-NRSfM, for NRSfM under conformal deformations.<n>Our approach performs point-wise reconstruction using 2D selected image warps optimized through a graph-based framework.<n>Our framework decouples constraints on depth and conformal scale, which are inseparable in other approaches.
  arXiv  Detail & Related papers  (2025-10-02T04:46:46Z)
• PHASE-Net: Physics-Grounded Harmonic Attention System for Efficient Remote Photoplethysmography Measurement [63.007237197267834]
  Existing deep learning methods are mostly physiological monitoring and lack theoretical robustness.<n>We propose a physics-informed r paradigm derived from the Navier-Stokes equations of hemodynamics, showing that the pulse signal follows a second-order system.<n>This provides a theoretical justification for using a Temporal Conal Network (TCN)<n>Phase-Net achieves state-of-the-art performance with strong efficiency, offering a theoretically grounded and deployment-ready r solution.
  arXiv  Detail & Related papers  (2025-09-29T14:36:45Z)
• Depth-Aware Super-Resolution via Distance-Adaptive Variational Formulation [5.1767204510818985]
  Single image super-resolution traditionally assumes spatially-invariant degradation models.<n>Real-world imaging systems exhibit complex distance-dependent effects including atmospheric scattering, depth-of-field variations, and perspective distortions.<n>We propose a rigorous variational framework that characterizes super-resolution as a spatially-varying inverse problem.
  arXiv  Detail & Related papers  (2025-09-06T15:35:37Z)
• A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging [8.436077464774755]
  Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns.<n>We present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging.
  arXiv  Detail & Related papers  (2024-11-25T06:49:59Z)
• Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo [52.68109922159688]
  We introduce a novel differentiable image formation model for active stereo, relying on both wave and geometric optics, and a novel trinocular reconstruction network. The jointly optimized pattern, which we dub "Polka Lines," together with the reconstruction network, achieve state-of-the-art active-stereo depth estimates across imaging conditions.
  arXiv  Detail & Related papers  (2020-11-26T04:02:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.