论文标题
通过学习形状先验完成点云
Point Cloud Completion by Learning Shape Priors
论文作者
论文摘要
考虑到重建对象详细信息的难度在点云完成中，我们提出了一种形状的先验学习方法，以完成对象完成。形状先验包括完整和部分点云中的几何信息。我们设计了一个功能对齐策略，可以从完整的点学习先验的形状，以及在精细阶段纳入部分先验的粗略策略。要先验到完整的对象，我们首先训练点云自动编码器，从完整的点提取潜在嵌入。然后，我们通过优化特征对准损失，学习了一个将点特征从部分点传递到完整点的映射。特征比对损耗由L2距离和通过最大平均差异生成对抗网络（MMD-GAN）获得的对抗损失组成。 L2距离优化了特征空间中完整距离的部分特征，MMD-GAN降低了复制的内核Hilbert空间中两个点特征的统计距离。我们在点云完成任务上实现最新的表演。我们的代码可在https://github.com/xiaogangw/point-cloud-completion-shape-prior上找到。
In view of the difficulty in reconstructing object details in point cloud completion, we propose a shape prior learning method for object completion. The shape priors include geometric information in both complete and the partial point clouds. We design a feature alignment strategy to learn the shape prior from complete points, and a coarse to fine strategy to incorporate partial prior in the fine stage. To learn the complete objects prior, we first train a point cloud auto-encoder to extract the latent embeddings from complete points. Then we learn a mapping to transfer the point features from partial points to that of the complete points by optimizing feature alignment losses. The feature alignment losses consist of a L2 distance and an adversarial loss obtained by Maximum Mean Discrepancy Generative Adversarial Network (MMD-GAN). The L2 distance optimizes the partial features towards the complete ones in the feature space, and MMD-GAN decreases the statistical distance of two point features in a Reproducing Kernel Hilbert Space. We achieve state-of-the-art performances on the point cloud completion task. Our code is available at https://github.com/xiaogangw/point-cloud-completion-shape-prior.
