Proceedings:
No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 36
Track:
AAAI Student Abstract and Poster Program
Downloads:
Abstract:
Point cloud has gained a lot of attention with the availability of a large amount of point cloud data and increasing applications like city planning and self-driving cars. However, current methods, often rely on labeled information and costly processing, such as converting point cloud to voxel. We propose a self-supervised learning approach to tackle these problems, combating labelling and additional memory cost issues. Our proposed method achieves results comparable to supervised and unsupervised baselines on the widely used benchmark datasets for self-supervised point cloud classification like ShapeNet, ModelNet10/40.
DOI:
10.1609/aaai.v36i11.21615
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 36