FastMESH: Fast Surface Reconstruction by Hexagonal Mesh-based Neural
  Rendering
Yisu Zhang Jianke Zhu Lixiang Lin
Despite the promising results of multi-view reconstruction, the recent neural
rendering-based methods, such as implicit surface rendering (IDR) and volume
rendering (NeuS), not only incur a heavy computational burden on training but
also have the difficulties in disentangling the geometric and appearance.
Although having achieved faster training speed than implicit representation and
hash coding, the explicit voxel-based method obtains the inferior results on
recovering surface. To address these challenges, we propose an effective
mesh-based neural rendering approach, named FastMESH, which only samples at the
intersection of ray and mesh. A coarse-to-fine scheme is introduced to
efficiently extract the initial mesh by space carving. More importantly, we
suggest a hexagonal mesh model to preserve surface regularity by constraining
the second-order derivatives of vertices, where only low level of positional
encoding is engaged for neural rendering. The experiments demonstrate that our
approach achieves the state-of-the-art results on both reconstruction and novel
view synthesis. Besides, we obtain 10-fold acceleration on training comparing
to the implicit representation-based methods.