论文标题
SSN：用于从点云的多类对象检测的形状签名网络
SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds
论文作者
论文摘要
多级3D对象检测旨在从点云本地化和分类多个类别的对象。由于点云的性质，即非结构化，稀疏和嘈杂，因此某些特征是益处的多类歧视，例如形状信息。在本文中，我们提出了一个新颖的3D形状签名，以探索点云中的形状信息。通过结合对称性，凸船体和Chebyshev拟合的操作，所提出的形状sig-nature不仅是紧凑且有效的，而且对噪声也是鲁棒的，这是一种软性约束，可以提高多级歧视的特征能力。基于提出的形状签名，我们开发了用于3D对象检测的形状签名网络（SSN），该网络由金字塔特征编码零件，形状感知的分组头和明确的形状编码目标组成。实验表明，所提出的方法的执行效果要比两个大规模数据集上的现有方法要好得多。此外，我们的形状签名可以充当插件组件，而消融研究显示出其有效性和良好的可扩展性
Multi-class 3D object detection aims to localize and classify objects of multiple categories from point clouds. Due to the nature of point clouds, i.e. unstructured, sparse and noisy, some features benefit-ting multi-class discrimination are underexploited, such as shape information. In this paper, we propose a novel 3D shape signature to explore the shape information from point clouds. By incorporating operations of symmetry, convex hull and chebyshev fitting, the proposed shape sig-nature is not only compact and effective but also robust to the noise, which serves as a soft constraint to improve the feature capability of multi-class discrimination. Based on the proposed shape signature, we develop the shape signature networks (SSN) for 3D object detection, which consist of pyramid feature encoding part, shape-aware grouping heads and explicit shape encoding objective. Experiments show that the proposed method performs remarkably better than existing methods on two large-scale datasets. Furthermore, our shape signature can act as a plug-and-play component and ablation study shows its effectiveness and good scalability
