Recent advancements in generative adversarial nets (GANs) and volumetric convolutional neural networks (CNNs) enable generating 3D models from a probabilistic space. In this paper, we have developed a novel GAN-based deep neural network to obtain a better latent space for the generation of 3D models. In the proposed method, an enhancer neural network is introduced to extract information from other corresponding domains (e.g. image) to improve the performance of the 3D model generator, and the discriminative power of the unsupervised shape features learned from the 3D model discriminator. Specifically, we train the 3D generative adversarial networks on 3D volumetric models, and at the same time, the enhancer network learns image features from rendered images. Different from the traditional GAN architecture that uses uninformative random vectors as inputs, we feed the high-level image features learned from the enhancer into the 3D model generator for better training. The evaluations on two large-scale 3D model datasets, ShapeNet and ModelNet, demonstrate that our proposed method can not only generate high-quality 3D models, but also successfully learn discriminative shape representation for classification and retrieval without supervision.