A Comparative Study of Coarse to Dense 3D Indoor Scene Registration Algorithms

TL;DR

This paper compares various 3D indoor scene registration algorithms, focusing on RGB-D data, to identify the most effective combinations for accurate and complete 3D reconstruction using affordable depth cameras.

Contribution

It provides a comprehensive evaluation of existing 3D registration methods across key steps, guiding better algorithm selection for indoor scene reconstruction.

Findings

Certain combinations of key point detection and description yield higher accuracy.

Refined alignment methods improve reconstruction completeness.

Evaluation results highlight trade-offs between computational cost and accuracy.

Abstract

3D alignment has become a very important part of 3D scanning technology. For instance, we can divide the alignment process into four steps: key point detection, key point description, initial pose estimation, and alignment refinement. Researchers have contributed several approaches to the literature for each step, which suggests a natural need for a comparative study for an educated more appropriate choice. In this work, we propose a description and an evaluation of the different methods used for 3D registration with special focus on RGB-D data to find the best combinations that permit a complete and more accurate 3D reconstruction of indoor scenes with cheap depth cameras.