To address the disparity calculation and 3D reconstruction problems for small textured objects, a PSMNet-ECSA algorithm based on a dual three-pooling attention mechanism was proposed. By embedding two dimensions of channel and spatial attention mechanisms into the backbone of the residual network, each dimension was fused using average pooling, maximum pooling, and mixed pooling techniques to merge feature dimensions, which prevented overfitting to some extent, thereby enhancing the network information extraction and generalization capabilities. Under the conditions of consistent experimental environments and datasets, an analysis was conducted through experiments on SceneFlow, KITTI2015, and real-world scenes. Compared to the original PSMNet algorithm, the proposed algorithm achieves a 10% improvement in metrics such as mean absolute errors and threshold errors. Applying this algorithm to reconstruct three-dimensional point cloud models of abalone, the average relative errors in distance measurements such as length, width, and breathing holes are within 3%, which can automatically measure and record the growth status of small marine organisms, and has promising practical application values.