  This paper is the first to propose an end-to-end framework of mutually
reinforcing images to 3D surface recurrent neural network-like for
model-adaptation indoor 3D reconstruction,where multi-view dense matching and
point cloud surface optimization are mutually reinforced by a RNN-like
structure rather than being treated as a separate issue.The characteristics are
as follows:In the multi-view dense matching module, the model-adaptation
strategy is used to fine-tune and optimize a Transformer-based multi-view dense
matching DNN,so that it has the higher image feature for matching and detail
expression capabilities;In the point cloud surface optimization module,the 3D
surface reconstruction network based on 3D implicit field is optimized by using
model-adaptation strategy,which solves the problem of point cloud surface
optimization without knowing normal vector of 3D surface.To improve and finely
reconstruct 3D surfaces from point cloud,smooth loss is proposed and added to
this module;The MRIo3DS-Net is a RNN-like framework,which utilizes the finely
optimized 3D surface obtained by PCSOM to recursively reinforce the
differentiable warping for optimizing MVDMM.This refinement leads to achieving
better dense matching results, and better dense matching results leads to
achieving better 3D surface results recursively and mutually.Hence,
model-adaptation strategy can better collaborate the differences between the
two network modules,so that they complement each other to achieve the better
effect;To accelerate the transfer learning and training convergence from source
domain to target domain,a multi-task loss function based on Bayesian
uncertainty is used to adaptively adjust the weights between the two networks
loss functions of MVDMM and PCSOM;In this multi-task cascade network
framework,any modules can be replaced by any state-of-the-art networks to
achieve better 3D reconstruction results.
