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arXiv · 2507.20632

Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend

Hongxu Liu·Xinyu Chen·Haoyang Zheng·Manyi Li·Zhenfan Liu·Fumeng Yang·Yunhai Wang·Changhe Tu·Qiong Zeng
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Abstract

Recovering a continuous colormap from a single 2D scalar field visualization can be quite challenging, especially in the absence of a corresponding color legend. In this paper, we propose a novel colormap recovery approach that extracts the colormap from a color-encoded 2D scalar field visualization by simultaneously predicting the colormap and underlying data using a decoupling-and-reconstruction strategy. Our approach first separates the input visualization into colormap and data using a decoupling module, then reconstructs the visualization with a differentiable color-mapping module. To guide this process, we design a reconstruction loss between the input and reconstructed visualizations, which serves both as a constraint to ensure strong correlation between colormap and data during training, and as a self-supervised optimizer for fine-tuning the predicted colormap of unseen visualizations during inferencing. To ensure smoothness and correct color ordering in the extracted colormap, we introduce a compact colormap representation using cubic B-spline curves and an associated color order loss. We evaluate our method quantitatively and qualitatively on a synthetic dataset and a collection of real-world visualizations from the VIS30K dataset. Additionally, we demonstrate its utility in two prototype applications -- colormap adjustment and colormap transfer -- and explore its generalization to visualizations with color legends and ones encoded using discrete color palettes.

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Hongxu Liu, Xinyu Chen, Haoyang Zheng, Manyi Li, Zhenfan Liu, Fumeng Yang, Yunhai Wang, Changhe Tu, Qiong Zeng. 2025-07-28. Self-Supervised Continuous Colormap Recovery from a 2D Scalar Field Visualization without a Legend. https://arxiv.org/abs/2507.20632

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