  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.
