# Joint Layout Estimation and Global Multi-View Registration for Indoor Reconstruction

## TL;DR

This paper introduces a joint approach for indoor 3D reconstruction that combines scene layout estimation with global multi-view registration, improving accuracy by iteratively refining both components.

## Contribution

The method innovatively integrates scene layout estimation with global registration in an iterative framework for enhanced indoor reconstruction accuracy.

## Key findings

- Effective in noisy real-world data
- Improves registration accuracy using layout constraints
- Validated on synthetic and real datasets

## Abstract

In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative fashion to complement each other. We extract the scene layout through hierarchical agglomerative clustering and energy-based multi-model fitting in consideration of noisy measurements. Having the estimated scene layout in one hand, we register all the range data through the global iterative closest point algorithm where the positions of 3D points that belong to the layout such as walls and a ceiling are constrained to be close to the layout. We experimentally verify the proposed method with the publicly available synthetic and real-world datasets in both quantitative and qualitative ways.