With the consumption of energy and land resources, the demands for offshore exploration and mining, infrastructure installation, maintenance, and repair continue to increase. Automated robotic platforms equipped with manipulators have emerged as a key solution, where operational precision relies heavily on the system′s environmental perception capabilities. However, object perception in underwater environments is considerably more challenging than in terrestrial environments, and optical imaging systems will fail in turbid waters. The underwater environment presents unstructured features, and low-light and turbid underwater environments pose significant challenges to the effectiveness and accuracy of optical perception. Although existing acoustic perception methods are robust, they suffer from limited resolution and incomplete spatial information, constraining high-precision manipulator servo control. To address these limitations, this article proposes a real-time, fine-resolution 3D ultrasonic imaging method for turbid water based on a sparse array. This method introduces the total focusing method (TFM), which is traditionally used in ultrasonic non-destructive testing, into marine perception for the first time. A uniform sparse sampling strategy is first applied to the matrix array to significantly reduce data volume and computational load. An adaptive directivity correction algorithm and a sign coherence factor weighting scheme are introduced to mitigate the sidelobe artifacts caused by periodic undersampling and large element sizes. The method is implemented on a GPU-based parallel computing platform to ensure real-time performance. Experiments are conducted on critical subsea components, a wet-mateable connector and valve, demonstrating that the proposed method effectively overcomes the limitations of optical imaging in turbid conditions and compensates for the resolution and geometric information deficiencies of existing acoustic perception approaches, achieving signal-to-noise ratios of 57.03 dB and 62.54 dB, respectively. This work offers a promising solution for refined environmental perception in robotic operations under extreme ocean conditions.