# Edge–Point Cloud Fusion for Geometric Fitting of Cylinder Parameters Using Single-View RGB-D Data

## TL;DR

This paper introduces a method that combines 2D edge information with 3D point cloud data to more accurately fit cylinder parameters from a single RGB-D camera view.

## Contribution

The novel approach fuses 2D edge constraints with point cloud data to improve cylinder parameter fitting in RGB-D data.

## Key findings

- The method achieves significant improvements in fitting accuracy for cylinders in real-world RGB-D data.
- Incorporating 2D edge information reduces the impact of noise in point cloud data.
- The approach demonstrates enhanced robustness compared to point cloud-only methods.

## Abstract

Cylinders are common in both industrial and daily settings. Accurate geometric fitting of their parameters, including position, orientation, and radius, is important in real-world perception tasks and industrial applications. At present, consumer-level RGB-D cameras provide three-dimensional (3D) point cloud data with acceptable accuracy and are widely adopted in various sensing applications. Consequently, this task is typically formulated as a geometric fitting problem based on point cloud data. However, point cloud data acquired from such sensors often contain noise, particularly when scanning curved surfaces, which directly degrades the performance of point cloud-based fitting methods. In this paper, we propose an edge–point cloud fusion approach for the geometric fitting of cylinder parameters from single-view RGB-D data. Our approach leverages two-dimensional (2D) image-domain edge constraints together with point cloud data, then fuses them in a unified formulation to jointly optimize cylinder parameters. By explicitly incorporating reliable edge information, our method effectively mitigates the effects of noise in point cloud data. We evaluate the proposed method using real-world RGB-D data, and the experimental results show that our approach achieves significant improvements in both accuracy and robustness.