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: We use ModelNet [33] for our training and testing datasets. ModelNet currently contains 127,915 3D CAD models from 662 categories. ModelNet40, a subset including 12,311 models from 40 categories, is well annotated and can be downloaded from the web. The authors also provide a training and testing split on the website, in which there are 9,843 training and 2,468 test models444VoxNet [24] uses the train/test split provided on the website and report average class accuracy on the 2,468 test split. 3DShapeNets [33] and MVCNN [32] use another train/test split comprising the first 80 shapes of each category in the “train” folder (or all shapes if there are fewer than 80) and the first 20 shapes of each category in the “test” folder, respectively.. We use this train/test split for our experiments. 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 did the authors chose the ModelNet dataset for evaluating the developed architectures ?
The authors follow previous works, such as VoxNet [24[, 3DShapeNets [33], and MVCNN [32] that also use ModelNet test set to evaluate their approaches. In order to be able to compare with them and provide more quantitative results, this paper also evaluates on ModelNet's test set. Additionally, as described in Table 1 (paragraph P7), the authors use ModelNet to provide additional evaluations and ablative analyses, which is more suitable to be done on synthetic data than RGB-D data.