Test-Time Augmentation for 3D Point Cloud Classification and
  Segmentation
Tuan-Anh Vu Srinjay Sarkar Zhiyuan Zhang Binh-Son Hua Sai-Kit Yeung
Data augmentation is a powerful technique to enhance the performance of a
deep learning task but has received less attention in 3D deep learning. It is
well known that when 3D shapes are sparsely represented with low point density,
the performance of the downstream tasks drops significantly. This work explores
test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent
revolution of learning implicit representation and point cloud upsampling,
which can produce high-quality 3D surface reconstruction and
proximity-to-surface, respectively. Our idea is to leverage the implicit field
reconstruction or point cloud upsampling techniques as a systematic way to
augment point cloud data. Mainly, we test both strategies by sampling points
from the reconstructed results and using the sampled point cloud as test-time
augmented data. We show that both strategies are effective in improving
accuracy. We observed that point cloud upsampling for test-time augmentation
can lead to more significant performance improvement on downstream tasks such
as object classification and segmentation on the ModelNet40, ShapeNet,
ScanObjectNN, and SemanticKITTI datasets, especially for sparse point clouds.