PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies

PointNet is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet has been largely surpassed by recent networks such as PointMLP and Point Transformer, we find that a large portion of the performance gain is due to improved training strategies, i.e. data augmentation and optimization techniques, and increased model sizes rather than architectural innovations. Thus, the full potential of PointNet has yet to be explored. In this work, we revisit the classical PointNet through a systematic study of model training and scaling strategies, and offer two major contributions. First, we propose a set of improved training strategies that significantly improve PointNet performance.