Current monocular 3D scene reconstruction (3DR) works are either
fully-supervised, or not generalizable, or implicit in 3D representation. We
propose a novel framework - MonoSelfRecon that for the first time achieves
explicit 3D mesh reconstruction for generalizable indoor scenes with monocular
RGB views by purely self-supervision on voxel-SDF (signed distance function).
MonoSelfRecon follows an Autoencoder-based architecture, decodes voxel-SDF and
a generalizable Neural Radiance Field (NeRF), which is used to guide voxel-SDF
in self-supervision. We propose novel self-supervised losses, which not only
support pure self-supervision, but can be used together with supervised signals
to further boost supervised training. Our experiments show that "MonoSelfRecon"
trained in pure self-supervision outperforms current best self-supervised
indoor depth estimation models and is comparable to 3DR models trained in fully
supervision with depth annotations. MonoSelfRecon is not restricted by specific
model design, which can be used to any models with voxel-SDF for purely
self-supervised manner.

通过自我监督的方法，基于体素有符号距离函数（voxel-SDF）实现了对室内场景的单目 RGB 视图下的 3D 网格重构，并且在实验中表现出与全监督模型和自监督室内深度估计模型相当的水平。