Deep Neural Networks (DNNs) exhibit notable susceptibility to adversarial attacks, which introduces substantial risks in safety-sensitive applications like self-driving vehicles and face identification. Unlike traditional targeted 2D attacks, targeted 3D adversarial attacks are more practical in real-world scenarios due to their effectiveness from multiple viewpoints. However, the transferability of adversarial examples generated by existing targeted 3D adversarial attack methods across different architectural models remains relatively limited. These methods inadequately capture the gradient differences among surrogate models, lack structured perturbation optimization, and fail to incorporate multi-view robustness constraints, thereby hindering the generalization of adversarial examples to unknown models and diverse physical conditions. To address these issues, we propose AIT3D-DSR, a novel method combining model integration with differentiable structured rendering. AIT3D-DSR employs a dynamic multi-model gradient integration strategy to align decision boundaries and incorporates block-level geometry-texture joint perturbations with physics-aware noise injection and randomized viewpoint transformations, thereby enhancing multi-view robustness. Comprehensive experiments show that AIT3D-DSR enhances the attack success rate by \(27\%\) compared to the best baseline, with an average success rate of \(86.80\%\) . It also demonstrates superior adversarial naturalness, as evidenced by a high SSIM value of 0.9002, a PSNR value of 30.59 dB, and a low LPIPS score of 0.0819. These results highlight AIT3D-DSR’s effectiveness in improving transferability while maintaining visual naturalness.