立体视觉以其低成本和高可靠性等优势，成为月面巡视探测中感知三维地形的重要技术路径。近年来，基于深度学习的立体匹配方法已成为实现高精度立体视觉的主流方案。然而，受限于数据采集条件，目前缺乏面向月球表面环境的公开立体匹配数据集，这严重制约了深度学习模型在月面场景中的训练与微调，进而影响其对月面复杂地形的适应能力。针对该问题，本文提出了一种基于三维渲染的立体匹配自监督学习方法Render3D。该方法只需单目月面环拍图像输入，通过融合Neural Radiance Fields（NeRF）的精确几何重建能力与2D Gaussian Splatting（2DGS）的高保真表面渲染能力，生成高质量伪标注训练样本，指导立体匹配模型进行微调以适配月面环境。在月面仿真环境与真实物理场景上的实验表明，经Render3D自监督学习后的立体匹配模型在精度上显著优于其他对比方法。在月面场景下，本方法明显领先于现有自监督学习方法，尤其在纹理稀疏与高阴影区域等复杂环境下，其匹配误差较基线方法降低约50%，达到SOTA性能。实验结果充分证实，Render3D方法能够有效缓解月面标注数据稀缺的限制，显著提升立体匹配模型在月面环境中的鲁棒性与泛化能力。

Stereo vision has emerged as a key technical approach for perceiving 3D terrain in lunar rover missions, owing to its advantages of low cost and high reliability. In recent years, deep learning-based stereo matching methods have become the mainstream solution for achieving high-precision stereo vision. However, limited by data acquisition constraints, there is currently a lack of public stereo matching datasets tailored to the lunar surface environment. This severely restricts the training and fine-tuning of deep learning models in lunar scenarios, thereby impairing their adaptability to complex lunar terrain. To address this issue, we propose Render3D, a 3D rendering-based self-supervised learning method for stereo matching. The proposed method requires only monocular panoramic lunar images as input. By integrating the high-fidelity surface rendering capability of 2D Gaussian Splatting (2DGS) with the accurate geometric reconstruction capability of Neural Radiance Fields (NeRF), it generates high-quality pseudo-annotated training samples. These samples guide the fine-tuning of the stereo matching model to adapt to the lunar environment. Experiments conducted in both simulated lunar environment and real physical scenario demonstrate that the stereo matching model fine-tuned using the self-supervised Render3D method significantly outperforms other methods in terms of accuracy. For lunar surface scenarios, the proposed method shows clear superiority over existing self-supervised learning approaches, particularly in challenging conditions such as textureless regions and areas with high shadows, where the matching error is reduced by approximately 50% compared to baseline methods, achieving state-of-the-art (SOTA) performance. Experimental results fully demonstrate that the Render3D method can effectively alleviate the constraint of scarce labeled data for lunar scenes, thus significantly improving the robustness and generalization ability of stereo matching models in the lunar environment.