3D Geometric Analysis of Tubular Objects based on Surface Normal Accumulation

TL;DR

This paper introduces a fast, versatile method for reconstructing and extracting geometric parameters of 3D tubular objects using surface normal accumulation, capable of processing various data types with high accuracy.

Contribution

It presents a novel, simple linear-time algorithm for tubular shape analysis and a new 3D torus detection technique based on surface normal accumulation.

Findings

Method is versatile and works with various input data types.

Achieves high accuracy and robustness in tubular shape extraction.

Can decompose tubes into rectilinear and torus-like parts efficiently.

Abstract

This paper proposes a simple and efficient method for the reconstruction and extraction of geometric parameters from 3D tubular objects. Our method constructs an image that accumulates surface normal information, then peaks within this image are located by tracking. Finally, the positions of these are optimized to lie precisely on the tubular shape centerline. This method is very versatile, and is able to process various input data types like full or partial mesh acquired from 3D laser scans, 3D height map or discrete volumetric images. The proposed algorithm is simple to implement, contains few parameters and can be computed in linear time with respect to the number of surface faces. Since the extracted tube centerline is accurate, we are able to decompose the tube into rectilinear parts and torus-like parts. This is done with a new linear time 3D torus detection algorithm, which follows the same principle of a previous work on 2D arc circle recognition. Detailed experiments show the versatility, accuracy and robustness of our new method.