基于分组的混合算法提升点云颜色一致性，效果优于当前最佳方法。
A Novel Grouping-Based Hybrid Color Correction Algorithm for Color Point Clouds
- 根据重叠率自适应分组目标点云，区分近、中、远距离区域
- 针对不同区域采用K近邻插值、联合直方图均衡等方法进行色彩修正
- 在1086组点云上验证性能，适合3D渲染与压缩场景
彩色点云的颜色一致性校正是在三维渲染和压缩应用中的基础且关键任务。以往多数方法针对彩色图像设计，本文提出一种基于分组的混合色彩校正算法。首先估计对齐源点云与目标点云之间的重叠率，据此自适应地将目标点划分为近邻组Gcl与中等邻近组Gmod，或进一步增加远邻组Gdist。对Gcl中的点采用基于K近邻的双边插值（KBI）方法；对Gmod中的点使用联合KBI与直方图均衡（JKHE）方法；对Gdist中的点则采用直方图均衡（HE）方法。最后讨论了算法的分组无关性及消融实验。通过1086组测试点云对当前最优方法的对比，验证了该算法在颜色一致性校正上的优越性。算法的C++源码可从https://github.com/ivpml84079/Point-cloud-color-correction 获取。
原文摘要 · Abstract (English)
Color consistency correction for color point clouds is a fundamental yet important task in 3D rendering and compression applications. In the past, most previous color correction methods aimed at correcting color for color images. The purpose of this paper is to propose a grouping-based hybrid color correction algorithm for color point clouds. Our algorithm begins by estimating the overlapping rate between the aligned source and target point clouds, and then adaptively partitions the target points into two groups, namely the close proximity group Gcl and the moderate proximity group Gmod, or three groups, namely Gcl, Gmod, and the distant proximity group Gdist, when the estimated overlapping rate is low or high, respectively. To correct color for target points in Gcl, a K-nearest neighbors based bilateral interpolation (KBI) method is proposed. To correct color for target points in Gmod, a joint KBI and the histogram equalization (JKHE) method is proposed. For target points in Gdist, a histogram equalization (HE) method is proposed for color correction. Finally, we discuss the grouping-effect free property and the ablation study in our algorithm. The desired color consistency correction benefit of our algorithm has been justified through 1086 testing color point cloud pairs against the state-of-the-art methods. The C++ source code of our algorithm can be accessed from the website: https://github.com/ivpml84079/Point-cloud-color-correction.
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