3D non-rigid registration using color: Color Coherent Point Drift

TL;DR

This paper introduces CCPD, a novel 3D non-rigid registration method that incorporates color information alongside geometry to improve correspondence accuracy in deforming structures, validated on synthetic and real RGB-D data.

Contribution

The paper presents CCPD, an extension of CPD that integrates color data for more robust 3D non-rigid registration, addressing challenges of noise and missing data.

Findings

CCPD outperforms original CPD in registration accuracy

Effective on synthetic data with noise, outliers, and missing data

Validated on real RGB-D data from Primesense Carmine sensor

Abstract

Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.