Abstract: In the field of 3D reconstruction of Yangtze finless porpoises, challenges such as underwater image color distortion, limited datasets, and difficulty in capturing multi-view images of Yangtze porpoises remain significant. Emerging methods have yet to address these issues specifically for Yangtze finless porpoises. To tackle these challenges, this paper proposes a novel single-view 3D reconstruction method for Yangtze finless porpoises, combining diffusion models and neural radiance fields. First, an improved underwater image enhancement technique is developed to effectively address the issue of underwater color distortion. Second, a custom multi-view image dataset of Yangtze finless porpoises is created to fine-tune a view-conditioned diffusion model, enabling the synthesis of multi-view images from a single view. This provides a new approach for reconstructing Yangtze finless porpoises from a single image. Finally, a neural radiance field is employed to reconstruct the 3D model of the porpoise. The reconstruction results were evaluated using the average chamfer distance (ACD) and normal consistency (NC). The proposed method achieved lower ACD and higher NC compared to existing methods, demonstrating its effectiveness in reconstructing 3D models that accurately capture the coloration and morphology of Yangtze finless porpoises. The synthesized multi-view images achieved PSNR, SSIM, and LPIPS values of 38.968, 0.972, and 0.294, respectively, surpassing the performance of existing methods. Additionally, the reconstruction results after underwater image enhancement yielded the lowest ACD of 0.428 and the highest NC of 0.882, further highlighting the superiority of the proposed approach.