We present a novel framework for mesh reconstruction from unstructured point
clouds by taking advantage of the learned visibility of the 3D points in the
virtual views and traditional graph-cut based mesh generation. Specifically, we
first propose a three-step network that explicitly employs depth completion for
visibility prediction. Then the visibility information of multiple views is
aggregated to generate a 3D mesh model by solving an optimization problem
considering visibility in which a novel adaptive visibility weighting in
surface determination is also introduced to suppress line of sight with a large
incident angle. Compared to other learning-based approaches, our pipeline only
exercises the learning on a 2D binary classification task, \ie, points visible
or not in a view, which is much more generalizable and practically more
efficient and capable to deal with a large number of points. Experiments
demonstrate that our method with favorable transferability and robustness, and
achieve competing performances \wrt state-of-the-art learning-based approaches
on small complex objects and outperforms on large indoor and outdoor scenes.
Code is available at this https URL

提出了一种从无结构点云中重建网格的新框架，通过利用虚拟视图中三维点的可见性和基于传统图割的网格生成。与其他基于学习的方法相比，该方法只在二维二分类任务上进行学习，更加普适和实用。实验表明，在小型复杂物体上，该方法具有良好的可传递性和鲁棒性，并与最先进的学习方法相比具有竞争优势，并且在大型室内和室外场景中表现出色。

Vis2Mesh: 从大型不规则点云中通过学习的虚拟视图可见性实现高效网格重构

Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds  of Large Scenes with Learned Virtual View Visibility

We address the task of converting a floorplan and a set of associated photos
of a residence into a textured 3D mesh model, a task which we call Plan2Scene.
Our system 1) lifts a floorplan image to a 3D mesh model; 2) synthesizes
surface textures based on the input photos; and 3) infers textures for
unobserved surfaces using a graph neural network architecture. To train and
evaluate our system we create indoor surface texture datasets, and augment a
dataset of floorplans and photos from prior work with rectified surface crops
and additional annotations. Our approach handles the challenge of producing
tileable textures for dominant surfaces such as floors, walls, and ceilings
from a sparse set of unaligned photos that only partially cover the residence.
Qualitative and quantitative evaluations show that our system produces
realistic 3D interior models, outperforming baseline approaches on a suite of
texture quality metrics and as measured by a holistic user study.

该论文介绍了 Plan2Scene 任务，旨在将一个住宅的平面图和一组相关的照片转换成有纹理的三维网格模型，解决了从一部分不对齐的照片中提取主要表面材质的挑战，并通过用户研究得出表现优异的结果。