@inproceedings{
zhang2024informative,
title={Informative Point cloud Dataset Extraction for Classification via Gradient-based Points Moving},
author={Wenxiao Zhang and Ziqi Wang and Li Xu and Xun Yang and Jun Liu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=RNj1rENImB}
}
Point cloud plays a significant role in recent learning-based vision tasks, which contain additional information about the physical space compared to 2D images. However, such a 3D data format also results in more expensive computation costs to train a sophisticated network with large 3D datasets. Previous methods for point cloud compression focus on compacting the representation of each point cloud for better storage and transmission. In this paper, we introduce a new open problem in point cloud field: Can we compress a large point cloud dataset into a much smaller synthetic dataset while preserving the important information of the original large dataset?} In other words, we explore the possibility of training a network on a smaller dataset of informative point clouds extracted from the original large dataset but maintaining similar network performance. Training on this small synthetic dataset could largely improve the training efficiency. To explore this new open problem, we formulate it as a parameter-matching issue where a network could get similar network parameters after training on the original set and the generated synthetic set, respectively. We find that we could achieve this goal by moving the critical points within each initial point cloud through an iterative gradient matching strategy. We conduct extensive experiments on various synthetic and real-scanned 3D object classification benchmarks, showing that our synthetic dataset could achieve almost the same performance with only 5\% point clouds of ScanObjectNN dataset compared to training with the full dataset.
Informative Point cloud Dataset Extraction for Classification via Gradient-based Points Moving
Wenxiao Zhang Ziqi Wang Li Xu Xun Yang Jun Liu