MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction

TL;DR

This paper introduces MRIo3DS-Net, an innovative end-to-end framework that mutually reinforces multi-view dense matching and 3D surface optimization for indoor 3D reconstruction, leveraging model adaptation and recursive refinement.

Contribution

It presents a novel RNN-like framework integrating multi-view dense matching and surface optimization with model adaptation, enhancing 3D reconstruction accuracy and efficiency.

Findings

Improved 3D surface reconstruction quality.

Effective mutual reinforcement between modules.

Faster convergence with Bayesian multi-task loss.

Abstract

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.