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The implicit neural representation of visual data (such as images, videos, and 3D models) has become a current hotspot in computer vision research. This work proposes a cover selection steganography scheme for neural radiance fields (NeRFs). The message sender first trains an NeRF model selecting any viewpoint in 3D space as the viewpoint keyKv, to generate a unique secret viewpoint image. Subsequently, a message extractor is trained using overfitting to establish a one-to-one mapping between the secret viewpoint image and the secret message. To address the issue of securely transmitting the message extractor in traditional steganography, the message extractor is concealed within a hybrid model performing standard classification tasks. The receiver possesses a shared extractor keyKe, which is used to recover the message extractor from the hybrid model. Then the secret viewpoint image is obtained by NeRF through the viewpoint keyKv, and the secret message is extracted by inputting it into the message extractor. Experimental results demonstrate that the trained message extractor achieves high-speed steganography with a large capacity and attains a 100% message embedding. Additionally, the vast viewpoint key space of NeRF ensures the concealment of the scheme.