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In this paper, we develop an inverse-diferentiable-rendering– based approach to acoustic-optical sensor fusion that can form dense 3D surface reconstructions from camera and sonar measure-ments
As for the “acoustic-optical fusion”, this article integrates measurements from multiple AUVs to obtain higher tracking accuracy. The collaborative effort of multiple AUVs overcomes individual
Our work develops a physics-based multimodal acoustic-optical neural surface reconstruction framework (AONeuS) capable of effectively integrating high-resolution RGB measurements with low-resolution
To overcome these issues, this study proposes an acoustic–optical fusion framework for underwater small-scale target detection, by incorporating result-based filtering through interactive multiple model
To address this challenge, this paper proposes a novel underwater acoustic-optical fusion-based underwater multi-modal object detection paradigm termed UAMFDet, which fuses highly misaligned
To address this challenge, this paper proposes a novel underwater acoustic-optical fusion-based underwater multi-modal object detection paradigm termed
By imitating the spiral geometry of the Nautilus, the regular metamaterial design strategy reduces the structural complexity and the sound loss. We introduce a multi-information fusion
Here we present a novel hybrid system, combining acoustic and optical technologies in a dynamic switching protocol guided by AI, reacting to environmental changes in real-time.