论文标题
预测零件的细化增强了点云完成
Refinement of Predicted Missing Parts Enhance Point Cloud Completion
论文作者
论文摘要
点云完成是通过使用3D形状的点集表示从部分观测来预测几何形状的任务。先前的方法提出了神经网络，可以通过不完整点集的编码器模型直接估算整个点云。通过预测完整的模型，当前方法计算冗余信息，因为输出还包含已知的不完整输入几何形状。本文提出了一个端到端神经网络体系结构，该架构着重于计算缺失的几何形状并合并已知输入和预测点云。我们的方法由两个神经网络组成：缺失的零件预测网络和合并的网络。第一个模块着重于从不完整输入中提取信息以推断缺失的几何形状。第二个模块合并点云并改善了点的分布。我们在Shapenet数据集上的实验表明，我们的方法在Point Cloud完成中的最新方法优于最新方法。我们的方法和实验的代码可在\ url {https://github.com/ivansipiran/refinement-point-point-cloud-completion}中获得。
Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. By predicting the complete model, the current methods compute redundant information because the output also contains the known incomplete input geometry. This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicted point cloud. Our method is composed of two neural networks: the missing part prediction network and the merging-refinement network. The first module focuses on extracting information from the incomplete input to infer the missing geometry. The second module merges both point clouds and improves the distribution of the points. Our experiments on ShapeNet dataset show that our method outperforms the state-of-the-art methods in point cloud completion. The code of our methods and experiments is available in \url{https://github.com/ivansipiran/Refinement-Point-Cloud-Completion}.
