MergeNet：通过边预测从稀疏点云进行显式网格重建
MergeNet: Explicit Mesh Reconstruction from Sparse Point Clouds via Edge Prediction
AI总结：
提出MergeNet，通过预测边连接将网格重建转为局部连接预测，利用边到表面距离过滤并细化三角剖分，在稀疏点云上优于现有显式方法。
摘要：
本文介绍了一种通过预测边连接从稀疏点云重建网格的新方法。现有的隐式方法由于使用等值面提取算法（如Marching Cubes）通常能产生平滑且水密的网格，但随着分辨率的提高，这些方法在内存和计算上变得密集。显式方法通过直接从点形成面更高效，然而，从大量候选中选择合适的面往往导致不理想的面和孔洞。此外，当点云变得稀疏时，两种方法的重建性能都会下降。为此，我们提出了通过边进行网格重建（MergeNet），将网格重建转化为局部连接预测问题。具体来说，MergeNet学习提取候选边的特征并回归它们到潜在表面的距离。因此，预测的距离被用来过滤掉位于表面上的边。最后，通过细化由这些边形成的三角剖分来重建网格。在合成和真实扫描数据集上的大量实验表明，MergeNet优于最先进的显式方法。
英文摘要：
This paper introduces a novel method for reconstructing meshes from sparse point clouds by predicting edge connection. Existing implicit methods usually produce superior smooth and watertight meshes due to the isosurface extraction algorithms~(e.g., Marching Cubes). However, these methods become memory and computationally intensive with increasing resolution. Explicit methods are more efficient by directly forming the face from points. Nevertheless, the challenge of selecting appropriate faces from enormous candidates often leads to undesirable faces and holes. Moreover, the reconstruction performance of both approaches tends to degrade when the point cloud gets sparse. To this end, we propose MEsh Reconstruction via edGE~(MergeNet), which converts mesh reconstruction into local connectivity prediction problems. Specifically, MergeNet learns to extract the features of candidate edges and regress their distances to the underlying surface. Consequently, the predicted distance is utilized to filter out edges that lay on surfaces. Finally, the meshes are reconstructed by refining the triangulations formed by these edges. Extensive experiments on synthetic and real-scanned datasets demonstrate the superiority of MergeNet to SoTA explicit methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
