Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP). Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models. We consequently aim to increase the success of the model by training it with more data. As suggested by the conventional ACGAN models, in addition to the real dataset, we train the Discriminator with synthetic data generated by the Generator using the class label. By doing so, we ensure that Discriminator can discriminate between the real data and the synthetic data. Thus, as the training evolves, the Generator is trained to produce more realistic synthetic data, which in turn forces Discriminator to classify or discriminate better. Defined originally on 2D images, our ACGAN-3D modifies this conventional ACGAN model in order to classify 3D point clouds by updating the neural network layers. Our second model VACWGAN-GP, on the other hand, demonstrates similar abilities with more stable training by replacing its Discriminator with Critic and by modifying its loss function. In this model, we managed to merge Wasserstein GAN-GP with conditional GAN in order to improve the classifier’s performance. The proposed models ACGAN-3D and VACWGAN-GP were tested extensively on 3D datasets and comparisons with the other state-of-the-art studies have revealed our clear advantages on various aspects. While ACGAN-3D can be preferred with its compact design, our second method VACWGAN-GP stands out for higher performance.