  Capturing and labeling real-world 3D data is laborious and time-consuming,
which makes it costly to train strong 3D models. To address this issue, recent
works present a simple method by generating randomized 3D scenes without
simulation and rendering. Although models pre-trained on the generated
synthetic data gain impressive performance boosts, previous works have two
major shortcomings. First, they focus on only one downstream task (i.e., object
detection), and the generalization to other tasks is unexplored. Second, the
contributions of generated data are not systematically studied. To obtain a
deeper understanding of the randomized 3D scene generation technique, we
revisit previous works and compare different data generation methods using a
unified setup. Moreover, to clarify the generalization of the pre-trained
models, we evaluate their performance in multiple tasks (i.e., object detection
and semantic segmentation) and with different pre-training methods (i.e.,
masked autoencoder and contrastive learning). Moreover, we propose a new method
to generate 3D scenes with spherical harmonics. It surpasses the previous
formula-driven method with a clear margin and achieves on-par results with
methods using real-world scans and CAD models.
