Kavli Affiliate: Wei Gao
| First 5 Authors: Zhiyi Pan, Guoqing Liu, Wei Gao, Thomas H. Li,
| Summary:
The acquisition of inductive bias through point-level contrastive learning
holds paramount significance in point cloud pre-training. However, the square
growth in computational requirements with the scale of the point cloud poses a
substantial impediment to the practical deployment and execution. To address
this challenge, this paper proposes an Effective Point-level Contrastive
Learning method for large-scale point cloud understanding dubbed
textbf{EPContrast}, which consists of AGContrast and ChannelContrast. In
practice, AGContrast constructs positive and negative pairs based on asymmetric
granularity embedding, while ChannelContrast imposes contrastive supervision
between channel feature maps. EPContrast offers point-level contrastive loss
while concurrently mitigating the computational resource burden. The efficacy
of EPContrast is substantiated through comprehensive validation on S3DIS and
ScanNetV2, encompassing tasks such as semantic segmentation, instance
segmentation, and object detection. In addition, rich ablation experiments
demonstrate remarkable bias induction capabilities under label-efficient and
one-epoch training settings.
| Search Query: ArXiv Query: search_query=au:”Wei Gao”&id_list=&start=0&max_results=3