Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured Environments

• URL: http://arxiv.org/abs/2603.13585v1
• Date: Fri, 13 Mar 2026 20:51:11 GMT
• Title: Sonar-MASt3R: Real-Time Opti-Acoustic Fusion in Turbid, Unstructured Environments
• Abstract summary: We propose opti-acoustic fusion method Sonar-MASt3R to extract dense correspondences from optical camera data in real-time.<n>The method pairs it with geometric cues from an acoustic 3D reconstruction to ensure robustness in turbid conditions.
• Score: 0.8594140167290097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater intervention is an important capability in several marine domains, with numerous industrial, scientific, and defense applications. However, existing perception systems used during intervention operations rely on data from optical cameras, which limits capabilities in poor visibility or lighting conditions. Prior work has examined opti-acoustic fusion methods, which use sonar data to resolve the depth ambiguity of the camera data while using camera data to resolve the elevation angle ambiguity of the sonar data. However, existing methods cannot achieve dense 3D reconstructions in real-time, and few studies have reported results from applying these methods in a turbid environment. In this work, we propose the opti-acoustic fusion method Sonar-MASt3R, which uses MASt3R to extract dense correspondences from optical camera data in real-time and pairs it with geometric cues from an acoustic 3D reconstruction to ensure robustness in turbid conditions. Experimental results using data recorded from an opti-acoustic eye-in-hand configuration across turbidity values ranging from <0.5 to >12 NTU highlight this method's improved robustness to turbidity relative to baseline methods.

Related papers

• ISOPoT: Imaging Sonar Odometry by Point Tracking [34.99229313882639]
  We introduce ISOPoT, an imaging sonar odometry method based on modern point tracking techniques.<n>We propose a sonar odometry pipeline that uses multi-frame point tracks as its primary correspondence representation.<n>Our results show that ISOPoT outperforms previous state-of-the-art methods consistently in both sonar-only scenarios and in multi-sensor settings.
  arXiv  Detail & Related papers  (2026-06-22T08:18:39Z)
• BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination [6.8920172327690175]
  We introduce BALTIC, a benchmark designed to evaluate modern 3D reconstruction methods under variations in medium and lighting.<n>The benchmark comprises 13 datasets spanning two media (air and water) and three lighting conditions (ambient, artificial, and mixed)<n>We investigate cross-domain reconstruction by augmenting underwater image sequences with a small number of in-air views captured under similar lighting conditions.
  arXiv  Detail & Related papers  (2026-04-21T06:32:07Z)
• Towards Versatile Opti-Acoustic Sensor Fusion and Volumetric Mapping [2.064612766965483]
  Volumetric mapping is critical for autonomous underwater vehicles.<n>vision-based perception provides high-resolution data but fails in turbid conditions.<n> sonar is robust to lighting and turbidity but suffers from low resolution and elevation ambiguity.<n>This paper presents a framework that fuses a stereo sonar pair with a monocular camera to enable safe navigation under varying visibility conditions.
  arXiv  Detail & Related papers  (2026-03-15T16:06:28Z)
• SonarSweep: Fusing Sonar and Vision for Robust 3D Reconstruction via Plane Sweeping [6.826863809223021]
  Single-modality approaches to 3D reconstruction fail due to poor visibility and geometric constraints.<n>Prior fusion technique relies on flawed geometrics, leading to significant artifacts and an inability to model complex scenes.<n>In this paper, we introduce SonarSweep, a novel, end-to-end deep learning framework that overcomes these limitations.
  arXiv  Detail & Related papers  (2025-11-01T04:12:27Z)
• Noise-adapted Neural Operator for Robust Non-Line-of-Sight Imaging [5.486845789695915]
  This paper presents a parameterized inverse problem framework tailored for large-scale linear problems in 3D imaging reconstruction.<n>A parameterized neural operator is developed to approximate the inverse mapping, facilitating end-to-end rapid image reconstruction.<n>Our 3D image reconstruction framework, grounded in operator learning, is constructed through deep algorithm unfolding.
  arXiv  Detail & Related papers  (2025-08-13T09:40:38Z)
• D3DR: Lighting-Aware Object Insertion in Gaussian Splatting [48.80431740983095]
  We propose a method, dubbed D3DR, for inserting a 3DGS-parametrized object into 3DGS scenes.<n>We leverage advances in diffusion models, which, trained on real-world data, implicitly understand correct scene lighting.<n>We demonstrate the method's effectiveness by comparing it to existing approaches.
  arXiv  Detail & Related papers  (2025-03-09T19:48:00Z)
• Can Optical Denoising Clean Sonar Images? A Benchmark and Fusion Approach [24.02055678758872]
  Object detection in sonar images is crucial for underwater robotics applications.<n>While denoising techniques have achieved remarkable success in optical imaging, their applicability to sonar data remains underexplored.<n>This study presents the first systematic evaluation of nine state-of-the-art deep denoising models with distinct architectures.
  arXiv  Detail & Related papers  (2025-03-03T15:30:39Z)
• Digging into contrastive learning for robust depth estimation with diffusion models [55.62276027922499]
  We propose a novel robust depth estimation method called D4RD. It features a custom contrastive learning mode tailored for diffusion models to mitigate performance degradation in complex environments. In experiments, D4RD surpasses existing state-of-the-art solutions on synthetic corruption datasets and real-world weather conditions.
  arXiv  Detail & Related papers  (2024-04-15T14:29:47Z)
• Realistic Noise Synthesis with Diffusion Models [44.404059914652194]
  Deep denoising models require extensive real-world training data, which is challenging to acquire.<n>We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Few-shot Non-line-of-sight Imaging with Signal-surface Collaborative Regularization [18.466941045530408]
  Non-line-of-sight imaging technique aims to reconstruct targets from multiply reflected light. We propose a signal-surface collaborative regularization framework that provides noise-robust reconstructions with a minimal number of measurements. Our approach has great potential in real-time non-line-of-sight imaging applications such as rescue operations and autonomous driving.
  arXiv  Detail & Related papers  (2022-11-21T11:19:20Z)
• 6D Camera Relocalization in Visually Ambiguous Extreme Environments [79.68352435957266]
  We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Our method achieves comparable performance with state-of-the-art methods on the indoor benchmark (7-Scenes dataset) using only 20% training data.
  arXiv  Detail & Related papers  (2022-07-13T16:40:02Z)
• Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
  We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise. It inherently generates accurate raw image noise for different camera sensors.
  arXiv  Detail & Related papers  (2021-10-10T10:45:24Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)

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.