LCAMV: High-Accuracy 3D Reconstruction of Color-Varying Objects Using LCA Correction and Minimum-Variance Fusion in Structured Light

TL;DR

This paper presents LCAMV, a novel 3D reconstruction method that corrects lateral chromatic aberration and adaptively fuses RGB data, achieving high accuracy without extra hardware or multiple exposures.

Contribution

Introduces LCAMV, a robust single-shot 3D reconstruction technique that models and compensates LCA and fuses multi-channel data using a noise-aware approach.

Findings

Reduces depth error by up to 43.6% compared to existing methods.

Operates with a single projector-camera pair, enabling fast acquisition.

Outperforms grayscale and conventional channel-weighting methods in accuracy.

Abstract

Accurate 3D reconstruction of colored objects with structured light (SL) is hindered by lateral chromatic aberration (LCA) in optical components and uneven noise characteristics across RGB channels. This paper introduces lateral chromatic aberration correction and minimum-variance fusion (LCAMV), a robust 3D reconstruction method that operates with a single projector-camera pair without additional hardware or acquisition constraints. LCAMV analytically models and pixel-wise compensates LCA in both the projector and camera, then adaptively fuses multi-channel phase data using a Poisson-Gaussian noise model and minimum-variance estimation. Unlike existing methods that require extra hardware or multiple exposures, LCAMV enables fast acquisition. Experiments on planar and non-planar colored surfaces show that LCAMV outperforms grayscale conversion and conventional channel-weighting, reducing depth error by up to 43.6\%. These results establish LCAMV as an effective solution for high-precision 3D reconstruction of nonuniformly colored objects.