论文标题
网格点云具有预测的固有 - 超级比率指导
Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance
论文作者
论文摘要
我们有兴趣从点云重建对象表面的网格表示。表面重建是下游应用程序的先决条件，例如渲染，避免计划，动画等。但是，如果输入点云具有低分辨率，那么任务是具有挑战性的，在现实世界中（例如，从激光雷达或kinect传感器中）很常见。现有的基于学习的网格生成方法主要通过首先构建整个对象级别的形状嵌入来预测表面，该设计在生成细粒细节并推广到看不见的类别时会引起问题。相反，我们建议通过仅向现有点添加连接信息，以尽可能多地利用输入点云。特别是，我们预测哪些点应形成面孔。我们的关键创新是局部连通性的替代品，可以通过比较内在/外在指标来计算。我们学会使用深点云网络预测这种替代物，然后将其馈送到有效的后处理模块，以实现高质量的网格生成。我们证明，我们的方法不仅可以保留细节，处理模棱两可的结构，而且还具有通过合成和真实数据实验的强大的普遍性来使人看不见类别。该代码可在https://github.com/colin97/point2mesh上找到。
We are interested in reconstructing the mesh representation of object surfaces from point clouds. Surface reconstruction is a prerequisite for downstream applications such as rendering, collision avoidance for planning, animation, etc. However, the task is challenging if the input point cloud has a low resolution, which is common in real-world scenarios (e.g., from LiDAR or Kinect sensors). Existing learning-based mesh generative methods mostly predict the surface by first building a shape embedding that is at the whole object level, a design that causes issues in generating fine-grained details and generalizing to unseen categories. Instead, we propose to leverage the input point cloud as much as possible, by only adding connectivity information to existing points. Particularly, we predict which triplets of points should form faces. Our key innovation is a surrogate of local connectivity, calculated by comparing the intrinsic/extrinsic metrics. We learn to predict this surrogate using a deep point cloud network and then feed it to an efficient post-processing module for high-quality mesh generation. We demonstrate that our method can not only preserve details, handle ambiguous structures, but also possess strong generalizability to unseen categories by experiments on synthetic and real data. The code is available at https://github.com/Colin97/Point2Mesh.
