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Point clouds have been a popular representation to describe 3D environments for autonomous driving applications. Despite accurate depth information, sparsity of points results in difficulties in extracting sufficient features from vulnerable objects of small sizes. One solution is leveraging self-attention networks to build long-range connections between similar objects. Another method is using generative models to estimate the complete shape of objects. Both approaches introduce large memory consumption and extra complexity to the models while the geometric characteristics of objects are overlooked. To overcome this problem, this paper proposes Point Augmentation (PA)-RCNN, focusing on small object detection by generating efficient complementary features without trainable parameters. Specifically, 3D points are sampled with the guidance of object proposals and encoded through the 3D grid-based feature …