A Novel Grouping-Based Hybrid Color Correction Algorithm for Color Point Clouds
Kuo-Liang Chung, Ting-Chung Tang
TL;DR
The paper tackles color inconsistencies in color point clouds after alignment by introducing a grouping-based hybrid color correction framework. It adaptively partitions target points into two or three groups based on the estimated overlap between source and target clouds, and applies group-specific corrections: KBI for close points, JKHE for moderately distant points, and HE for distant points, with a grouping-effect free property ensuring boundary smoothness. The method is validated on 1086 testing pairs from SHOT and augmented ICL-NUIM datasets, achieving the best average CMD and near-best CPSNR compared with state-of-the-art approaches, and is supported by comprehensive ablation studies. The approach advances practical color correction for 3D color point clouds and provides open-source code for reproducibility and integration into 3D rendering and compression pipelines.
Abstract
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.