论文标题
Pointtree：带有放松的K-d树的转换式点云编码器
PointTree: Transformation-Robust Point Cloud Encoder with Relaxed K-D Trees
论文作者
论文摘要
能够直接在原始点云上学习有效的语义表示已成为3D理解中的一个核心话题。尽管进步迅速，但最新的编码器仍限制了典型的点云，并且在遇到几何变形扭曲时的性能弱于必要的性能。为了克服这一挑战，我们提出了Point-Stree，这是一种通用点云编码器，对基于放松的K-D树的转换非常可靠。我们方法的关键是使用主成分分析（PCA）在K-d树中设计了分区规则。我们将放松的K-D树的结构用作计算图，并将特征作为边框描述符建模，并将其与点式最大运算合并。除了这种新颖的体系结构设计外，我们还通过引入预先对准进一步提高了鲁棒性 - 一种简单但有效的基于PCA的标准化方案。我们的PointTree编码器与预先对齐的结合始终优于大幅度的最先进方法，用于从对象分类到广泛基础的数据集的各种转换版本的语义分割的应用程序。代码和预培训模型可在https://github.com/immortalco/pointtree上找到。
Being able to learn an effective semantic representation directly on raw point clouds has become a central topic in 3D understanding. Despite rapid progress, state-of-the-art encoders are restrictive to canonicalized point clouds, and have weaker than necessary performance when encountering geometric transformation distortions. To overcome this challenge, we propose PointTree, a general-purpose point cloud encoder that is robust to transformations based on relaxed K-D trees. Key to our approach is the design of the division rule in K-D trees by using principal component analysis (PCA). We use the structure of the relaxed K-D tree as our computational graph, and model the features as border descriptors which are merged with pointwise-maximum operation. In addition to this novel architecture design, we further improve the robustness by introducing pre-alignment -- a simple yet effective PCA-based normalization scheme. Our PointTree encoder combined with pre-alignment consistently outperforms state-of-the-art methods by large margins, for applications from object classification to semantic segmentation on various transformed versions of the widely-benchmarked datasets. Code and pre-trained models are available at https://github.com/immortalCO/PointTree.
