解决彩色物体3D扫描中色差问题，提升重建精度。
LCAMV: High-Accuracy 3D Reconstruction of Color-Varying Objects Using LCA Correction and Minimum-Variance Fusion in Structured Light
- 通过像素级校正投影与相机的横向色差
- 利用最小方差融合降低多通道噪声，深度误差减少43.6%
- 单套设备快速扫描，适合复杂色彩物体
基于结构光的彩色物体高精度三维重建受光学元件横向色差（LCA）及各RGB通道噪声不均影响。本文提出一种无需额外硬件或多重曝光的鲁棒重建方法——LCAMV，该方法在单个投影-相机系统下，对投影仪和相机中的LCA进行像素级解析建模与补偿，并基于泊松-高斯噪声模型与最小方差估计，自适应融合多通道相位数据。实验表明，该方法在平面与非平面彩色表面测试中，相比灰度转换与传统通道加权法，深度误差最高降低43.6%。结果证明LCAMV是实现非均匀彩色物体高精度3D重建的有效方案。
原文摘要 · Abstract (English)
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.
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