The integration of Novel View Synthesis (NVS) and 3D display technology opens unprecedented opportunities for 3D visualization across diverse fields. However, generating high-quality 3D content in complex, detail-intensive scenarios remains challenging due to high production costs and insufficient stereoscopic detail rendering in existing methods. In response, we propose a new rendering framework (Stereo3D-NeRF) that leverages pairwise left-right eye inputs and implicitly introduces disparity, enabling the network to capture richer spatial depth and structure without explicit disparity maps. By exploiting natural viewpoint differences for pixel-level matching, Stereo3D-NeRF significantly improves the reconstruction of fine textures and sharp edges, resulting in more immersive and realistic 3D content. Experimental results show that this strategy greatly improves the reconstruction quality of complex model surfaces and textures, while demonstrating outstanding fidelity in a range of 3D visualization tasks, laying a solid foundation for broader adoption of novel view synthesis.