This paper presents Neural Mesh Fusion (NMF), an efficient approach for joint
optimization of polygon mesh from multi-view image observations and
unsupervised 3D planar-surface parsing of the scene. In contrast to implicit
neural representations, NMF directly learns to deform surface triangle mesh and
generate an embedding for unsupervised 3D planar segmentation through
gradient-based optimization directly on the surface mesh. The conducted
experiments show that NMF obtains competitive results compared to
state-of-the-art multi-view planar reconstruction, while not requiring any
ground-truth 3D or planar supervision. Moreover, NMF is significantly more
computationally efficient compared to implicit neural rendering-based scene
reconstruction approaches.

该研究提出了神经网格融合（NMF），这是一个高效的方法，用于联合优化多视角图像观测的多边形网格和场景的无监督 3D 平面分割。与隐式神经表示不同，NMF 直接学习变形表面三角网格，并通过基于梯度的优化在表面网格上生成无监督 3D 平面分割的嵌入。实验证明，与最先进的多视角平面重建方法相比，NMF 获得了有竞争力的结果，同时不需要任何 3D 或平面的真实监督。此外，与基于隐式神经渲染的场景重建方法相比，NMF 在计算上更加高效。