Improving Neural Indoor Surface Reconstruction with Mask-Guided Adaptive
  Consistency Constraints
Xinyi Yu Liqin Lu Jintao Rong Guangkai Xu Linlin Ou
3D scene reconstruction from 2D images has been a long-standing task. Instead
of estimating per-frame depth maps and fusing them in 3D, recent research
leverages the neural implicit surface as a unified representation for 3D
reconstruction. Equipped with data-driven pre-trained geometric cues, these
methods have demonstrated promising performance. However, inaccurate prior
estimation, which is usually inevitable, can lead to suboptimal reconstruction
quality, particularly in some geometrically complex regions. In this paper, we
propose a two-stage training process, decouple view-dependent and
view-independent colors, and leverage two novel consistency constraints to
enhance detail reconstruction performance without requiring extra priors.
Additionally, we introduce an essential mask scheme to adaptively influence the
selection of supervision constraints, thereby improving performance in a
self-supervised paradigm. Experiments on synthetic and real-world datasets show
the capability of reducing the interference from prior estimation errors and
achieving high-quality scene reconstruction with rich geometric details.