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

Wonbeen Oh; Jae-Sang Hyun

cs.CVMar 11, 2026

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

Authors: Wonbeen Oh, Jae-Sang Hyun

Organizations: Department of Mechanical Engineering, Yonsei University, Seoul 03722, South Korea.

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.

Figures & tables

Fig. 1: LCA on scanning a colored object in DFP system.

Fig. 2: Structured light system setup

Fig. 3: Aberration in camera system

Fig. 4: Aberration in projector system

Fig. 5: Pixel-wise Pearson correlation map of $\Delta_{O}$ and $z$ from channel (a) red (b) blue

Panel of Figure 6 — Fig. 6: Projector LCA coefficient map (a) $\alpha^{R}$ (b) $\alpha^{B}$ (c) $\beta^{R}$ (d) $\beta^{B}$

	Red	Green	Blue
$k_{0}$	0.1333	0.1184	0.1500
$k_{1}$	0.0215	0.0134	0.0170

TABLE I: Calibrated RGB Noise Coefficient

Fig. 8: Standard deviation and confidence interval of $\phi$

Fig. 9: $\hat{\Delta}_{O},\Delta_{O}$ while scanning white sphere and sculpture (a) $\hat{\Delta}_{O}^{R}$ (b) $\Delta_{O}^{R}$ (c) $\hat{\Delta}_{O}^{B}$ (d) $\Delta_{O}^{B}$

Fig. 10: Image of White Sphere and Sculpture

Fig. 11: Colorboard plane fitting error map using (a) LCAMV (b) Mean (c) Y’UV (d) Green

Method	MSE ( $\text{mm}^{2}$ )
	LCAMV	Mean	Y’UV	Green
3-Step	0.036073	0.055645	0.064706	0.082067
12-Step	0.014794	0.027995	0.029057	0.038311
18-Step	0.012485	0.024468	0.024343	0.032854

TABLE II: Colorboard Plane Fitting MSE

Panel of Figure 15 — Fig. 13: RGB board rendering comparison using (a) LCAMV (b) LCA (c) MV

Method	MSE ( $\text{mm}^{2}$ )
	LCAMV	LCA (Abl.)	MV (Abl.)
3-Step	0.030582	282.76	0.066288
12-Step	0.016121	295.12	0.054010
18-Step	0.014217	299.32	0.052371

TABLE III: RGB board Plane Fitting MSE

Panel of Figure 18 — Fig. 15: Curvy RGB surface rendering comparison using (a) LCAMV (b) Mean (c) Green

Panel of Figure 19 — Fig. 16: Zigzagged RGB surface rendering comparison using (a) LCAMV (b) Mean (c) Green

Fig. 17: ROI and image of RGB surface (a) curvy (b) zigzagged

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Abstract

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