You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Compared with 2D image datasets, currently available 3D shape datasets are limited in scale and variation. To fully exploit the design of our networks, we augment the training data with different azimuth and elevation rotations.This allows the first network to cover local regions at different orientations, and the second network to relate distant points at different relative angles. Compared to 3DShapeNets [33] which only augments data by rotating around vertical axis, our experiment shows that orientation pooling combined with elevation rotation can greatly increase performance. We use the same volumetric CNN model, the end-to-end learning verion of 3DShapeNets [33], to train and test on three variations of augmented data (Table 1). Similar trend is observed for other volumetric CNN variations.

Question: Why didn't the authors try different methods of data augmentation for 3D objects ?
The authors improve upon previous augmentation strategies and provide analyses to compare each combination of the augmentation strategy (azimuth rotation (AZ), AZ + translation, and AZ + elevation rotation), and conclude that the latter gives the best results. Please note that augmentations on 3D objects are not as trivial as the ones in 2D so providing novel insights w.r.t. data augmentation is quite valuable. I am not sure what would be the possible additional augmentation method in 3D that authors should have tried...