Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding
<|reference_start|>Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding: 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.<|reference_end|>
@article{zanjani2024neural,
    title={Neural Mesh Fusion: Unsupervised 3D Planar Surface Understanding},
    author={Farhad G. Zanjani, Hong Cai, Yinhao Zhu, Leyla Mirvakhabova, Fatih
  Porikli},
    journal={arXiv preprint arXiv:2402.16739},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16739},
    primaryClass={cs.CV}
}