Image-based 3D reconstruction is the process of producing 3D representations of an object based on its single or multiple images. Existing methods for 3D reconstruction can directly learn to transform image features into 3D representations, using encoder-decoder structure, combined with binary cross entropy function and its deformation. However, the encoder cannot extract enough information from images to reconstruct high-quality 3D shapes, resulting in inaccurate Geometric details of reconstructed 3D objects. The loss functions based on the binary cross entropy function underperforms in target perception when the voxel distribution is imbalanced, leading to problems of incompleteness such as fractures and missing in the reconstruction results. To address these problems, a structure and target-aware 3D object reconstruction framework was proposed for single-view and multi-view 3D reconstruction, named ST-Rec3D. Combined with attention mechanism, we designed an encoder with a spatial perception structure, namely structure-aware encoder. In doing so, the spatial structure information could be fully captured in the input image and the local details of the reconstructed object could be effectively perceived. The utilization of IoU loss in the 3D voxel reconstruction, in the case of uneven voxel distribution, could accurately perceive the target object to ensure the integrity and accuracy of the reconstructed object. Experimental results demonstrate that ST-Rec3D can give a significant boost to reconstruction quality and outperform state-of-the-art methods on the ShapeNet and Pix3D.