解决360°场景3D着色中颜色单一问题，提升色彩多样性与一致性。
LoGoColor: Local-Global 3D Colorization for 360° Scenes
- 采用局部-全局策略分割场景，分块处理颜色一致性。
- 相比传统方法，颜色更丰富且多视角保持一致。
- 适合需要高保真视觉效果的全景3D重建应用。
单通道3D重建广泛应用于机器人和医学成像等领域。尽管这些方法能有效重建3D几何结构，但输出通常为无色3D模型，需进行3D着色以实现可视化。现有3D着色研究通过蒸馏2D图像着色模型来解决该问题，但其固有的2D模型一致性缺陷导致训练时颜色被平均化，尤其在复杂360°场景中出现色彩单调、简化的问题。为此，我们提出新方法，通过生成一组一致着色的训练视图，消除引导平均过程，以保留颜色多样性。然而，这引入了多视图一致性挑战。为此，我们设计了一种局部-全局管道：将场景划分为子区域，利用微调的多视图扩散模型分别处理子间与子内一致性。实验表明，本方法在复杂360°场景中实现了比现有方法更一致且更逼真的3D着色结果。
原文摘要 · Abstract (English)
Single-channel 3D reconstruction is widely used in fields such as robotics and medical imaging. While these methods are good at reconstructing 3D geometry, their outputs are typically uncolored 3D models, making 3D colorization necessary for visualization. Recent 3D colorization studies address this problem by distilling 2D image colorization models. However, these approaches suffer from an inherent inconsistency of 2D image models. This results in colors being averaged during training, leading to monotonous and oversimplified results, particularly in complex 360° scenes. In contrast, we aim to preserve color diversity by generating a new set of consistently colorized training views, thereby suppressing the averaging process. Nevertheless, mitigating the averaging process introduces a new challenge: ensuring strict multi-view consistency across these colorized views. To achieve this, we propose \ourmethod, a pipeline designed to preserve color diversity by eliminating this guidance-averaging process with a `Local-Global' approach: we partition the scene into subscenes and explicitly tackle both inter-subscene and intra-subscene consistency using a fine-tuned multi-view diffusion model. We demonstrate our method achieves quantitatively and qualitatively more consistent and plausible 3D colorization on complex 360° scenes than existing methods.
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