AquaBEV is a monocular underwater occupancy model that predicts local bird’s‑eye‑view (BEV) occupancy from a single RGB image. It uses paired 3D imaging sonar data as geometric supervision during training, mapping visual features into a calibration‑free polar representation and decoding along the range dimension before reconstructing Cartesian BEV coordinates. In a controlled underwater occupancy benchmark, AquaBEV outperforms the strongest transferred baseline with 31.4 % Visible IoU and 38.6 % Observed IoU, achieving 4.0 % and 4.3 % relative improvements respectively.
By Trung Tien Dong, Shengji Jin, Chen Chen, Yi Sheng, Xiaomin Lin
RSFusionDet introduces a new RGB‑Sonar multimodal object detection dataset (RSFusion) and benchmark metrics for underwater imaging. The proposed detector uses a Cross‑Attention Fusion module to align RGB and sonar features and an Object Matching Head with loss to identify identical objects across modalities. On the RSFusion dataset, RSFusionDet achieves 76.4/48.6 AP for RGB/sonar detection and 83.4 F1‑Score for cross‑modal matching, outperforming existing models and improving over the DINO baseline by 0.7/1.4 AP.
By Zhuoyan Liu, Yihan Wang, Bo Wang, Bing Wang, Ye Li
arXiv:2608.29433v1 Announce Type: new
Abstract: Sonars generate a significant amount of noise. With the advent of new technology capable of producing full 3D point clouds, the noise is amplified in s...
By Aditya Penumarti, Khanh Dong, Zi-Hao Zhang, Yongkyoon Park, Zhenqi Wu, Trung Dong, Shahriar Negahdaripour, Xiaomin Lin, Jane Shin
SonarLLM is a multimodal large language model that treats sonar as a native perceptual modality, combining a sonar‑specific encoder, physics‑aware feature enhancement, and reliability‑aware hierarchical fusion to align acoustic structure with optical semantics. The authors introduce SonarBench, a benchmark covering recognition, counting, visual question answering, and captioning across sonar‑only, optical‑only, and fusion settings, enabling controlled measurement of cross‑modal complementarity. SonarLLM achieves 72.0% macro accuracy on sonar‑only tasks and 68.7% under fusion, outperforming baselines by significant margins and demonstrating increasing fusion gains as optical visibility degrades.
By Cong Su, longxuan ma, Ling Dong, Guofeng Tang, Weijie Yin, Haohui Chen, Zhengtao Yu
arXiv:2510. 04876v3 Announce Type: replace-cross Abstract: Benthic habitat mapping is fundamental for understanding marine ecosystems, guiding conservation efforts, and supporting sustainable resource management.
By Hayat Rajani, Valerio Franchi, Borja Martinez-Clavel Valles, Raimon Ramos, Rafael Garcia, Nuno Gracias
Estimating 3D geometry in underwater environments presents unique challenges due to light attenuation, scattering, and the absence of large-scale, high-quality 3D annotations. Pioneering methods rely on massive dense annotations that are impractical in underwater settings.