论文标题
3D PointCloud图创建的学习特定任务功能
Learning task-specific features for 3D pointcloud graph creation
论文作者
论文摘要
使用深度学习方法处理3D PointCloud并不是一件容易的事。一个常见的选择是使用图形神经网络这样做，但是该框架涉及在点之间的边缘创建边缘，这些边缘在它们之间明确无关。从历史上看，已经提出了幼稚和手工制作的方法，例如K最近的邻居（K-NN）或XYZ特征上的查询球点，而不是改善图表，将更多的关注放在改善网络上。在这项工作中，我们提出了一种从3D PointCloud创建图形的更有原则的方法。我们的方法基于对输入3D PointCloud的转换执行K-NN。这种转换是由具有可学习参数的多层感知器（MLP）完成的，该参数通过与网络的其余部分共同通过反向传播进行了优化。我们还基于应力最小化引入了一种正则化方法，该方法可以控制我们基线的距离距离：k-nn在XYZ空间上。该框架在ModelNet40上进行了测试，其中由我们的网络生成的图表优于基线的总准确性0.3点。
Processing 3D pointclouds with Deep Learning methods is not an easy task. A common choice is to do so with Graph Neural Networks, but this framework involves the creation of edges between points, which are explicitly not related between them. Historically, naive and handcrafted methods like k Nearest Neighbors (k-NN) or query ball point over xyz features have been proposed, focusing more attention on improving the network than improving the graph. In this work, we propose a more principled way of creating a graph from a 3D pointcloud. Our method is based on performing k-NN over a transformation of the input 3D pointcloud. This transformation is done by an Multi-Later Perceptron (MLP) with learnable parameters that is optimized through backpropagation jointly with the rest of the network. We also introduce a regularization method based on stress minimization, which allows to control how distant is the learnt graph from our baseline: k-NN over xyz space. This framework is tested on ModelNet40, where graphs generated by our network outperformed the baseline by 0.3 points in overall accuracy.
