Technology Category
Computer Vision: 3D Computer VisionConstraint Satisfaction and Optimization: Distributed CSP/OptimizationSearch and Optimization: Mixed Discrete/Continuous Search
A Novel Grouping-Based Hybrid Color Correction Algorithm for Color Point Clouds
🤖 AI Summary
Color consistency correction for colored point clouds is a critical preprocessing step for 3D rendering and compression. This paper proposes a hybrid color correction algorithm based on overlap-ratio-adaptive grouping: the target point cloud is partitioned into three subsets—near-neighbor, mid-neighbor, and far-neighbor—according to inter-cloud overlap ratios; each subset is then corrected using distinct strategies—K-nearest-neighbor bilateral interpolation (KBI), joint KBI and histogram equalization (JKHE), and histogram equalization (HE), respectively. Crucially, we introduce the “group-invariance property” analysis for the first time to systematically guide strategy selection. Evaluated on 1,086 point cloud pairs, our method significantly outperforms existing state-of-the-art approaches, achieving superior color consistency while preserving geometric fidelity and improving computational efficiency.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Corrects color inconsistencies in colored 3D point cloud data
Groups point clouds by proximity for adaptive color correction
Uses hybrid methods combining bilateral interpolation and histogram equalization
Innovation
Methods, ideas, or system contributions that make the work stand out.
Grouping point clouds by proximity for adaptive correction
Using K-nearest neighbors bilateral interpolation for close points
Combining histogram equalization with interpolation for moderate points
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