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

2026-03-11

AI Summary

This work addresses the degradation in 3D reconstruction accuracy of structured light systems when scanning colored objects, primarily caused by lateral chromatic aberration (LCA) at both the projector and camera ends, as well as non-uniform noise across RGB channels. The authors propose a single-shot, hardware-free solution that jointly models and corrects LCA at the pixel level for both projection and imaging paths. By integrating a Poisson–Gaussian noise model with minimum-variance estimation, the method adaptively fuses phase information from multiple color channels. Experimental results demonstrate that this approach reduces depth errors by up to 43.6% on both planar and non-planar colored surfaces compared to conventional grayscale conversion and weighted fusion techniques, substantially enhancing reconstruction fidelity.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• 3D reconstruction
• structured light
• lateral chromatic aberration
• color-varying objects
• noise characteristics

Innovation

• lateral chromatic aberration correction
• minimum-variance fusion
• structured light
• 3D reconstruction
• Poisson-Gaussian noise model