论文标题
使用基于梯度的方法的3D点云特征解释
3D Point Cloud Feature Explanations Using Gradient-Based Methods
论文作者
论文摘要
解释性是推动用户对使用神经网络用于具有物质影响的任务的重要因素。但是，该领域所做的大多数工作都集中在图像分析上，并且没有考虑到3D数据。我们扩展了已显示在图像数据上处理3D数据的显着方法。我们分析了点云和体素空间中的特征，并表明3D数据中的边缘和角落被视为重要特征，而平面表面的重要性不大。该方法是模型不合时宜的，可以提供有关学习特征的有用信息。在3D数据本质上稀疏的洞察力的驱动下，我们可视化基于体素的分类网络所学到的功能，并表明这些功能也很稀疏，并且可以相对容易地修剪，从而导致更有效的神经网络。我们的结果表明，可以将Voxception-Resnet模型修剪至其参数的5％，而准确性损失可忽略不计。
Explainability is an important factor to drive user trust in the use of neural networks for tasks with material impact. However, most of the work done in this area focuses on image analysis and does not take into account 3D data. We extend the saliency methods that have been shown to work on image data to deal with 3D data. We analyse the features in point clouds and voxel spaces and show that edges and corners in 3D data are deemed as important features while planar surfaces are deemed less important. The approach is model-agnostic and can provide useful information about learnt features. Driven by the insight that 3D data is inherently sparse, we visualise the features learnt by a voxel-based classification network and show that these features are also sparse and can be pruned relatively easily, leading to more efficient neural networks. Our results show that the Voxception-ResNet model can be pruned down to 5\% of its parameters with negligible loss in accuracy.
