Compared to traditional 3D reconstruction methods, neural radiance fields (NeRFs) can effectively capture implicit neural representations, enabling high-quality 3D reconstruction and novel view synthesis tasks. However, NeRFs typically require a large amount of raw data for training. To this end, this study proposes a method by integrating variational autoencoders (VAEs) with NeRF to improve 3D scene generation performance under limited training data. Firstly, by constructing a VAE encoder, a certain proportion of raw images from the training data are selected to form a vector set. Meanwhile, the encoder compresses this set to capture latent feature vectors, which are then employed to supplement global scene information in the input layer. Secondly, an adaptive-enhanced sampling algorithm is developed to dynamically adjust the distribution density of sampling points, thereby improving NeRF’s ability to capture fine scene details. Experiments conducted on three public datasets demonstrate the effectiveness of the proposed method. Additionally, the proposed method achieves 3D reconstruction results comparable to baseline methods trained with full datasets, even under the loss of original training data.