Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding
Farhad G. Zanjani Hong Cai Yinhao Zhu Leyla Mirvakhabova Fatih Porikli
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.