用熵值识别模糊场景中的干扰物，实现更干净的3D高斯渲染。
Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios
- 基于熵和实例掩码动态生成遮罩，精准捕捉模糊干扰物。
- 在18个复杂场景上验证，显著提升模糊环境下的新视角合成质量。
- 适合做3D重建与视觉编辑的研究者，尤其关注干扰物处理问题。
我们提出RefineSplat，一个系统性框架，通过有效构建瞬态掩码来识别多样化的模糊干扰物。为此，我们定性和定量分析了现有方法的问题，并提出一种新型的熵感知自适应掩码方法。与以往难以区分瞬态元素与静态场景的方法不同，RefineSplat利用熵值和实例掩码捕捉模糊干扰物。此外，我们设计了一种简单但有效的熵感知密度控制策略，结合熵感知位置梯度，在模糊场景中对齐高斯分布。为严格验证方法，我们首次构建并发布Ambiguous wild数据集，包含18个因颜色或语义相似而难以区分干扰物与静态场景的场景。在多个数据集上的实验表明，RefineSplat达到当前最优性能，实现了无干扰的新视角合成。
原文摘要 · Abstract (English)
We present RefineSplat, a systematic framework that effectively constructs transient masks to identify diverse ambiguous distractors. To do this, we qualitatively and quantitatively analyze issues and propose a novel entropy-aware adaptive masking method. Unlike existing approaches that struggle to distinguish transient elements from static scenes due to color or semantic ambiguity, RefineSplat captures ambiguous distractors leveraging entropy and instance masks. Furthermore, we propose a simple yet effective entropy-aware density control to align Gaussians in ambiguous scenarios considering Entropy-aware positional gradients. Additionally, to rigorously validate our method, we first create and release the Ambiguous wild dataset, including 18 scenes where distractors and static scenes are hard to distinguish due to color or semantic resemblances. Experimental results on various datasets demonstrate that RefineSplat shows state-of-the-art performance, showing distractor-free novel view synthesis.
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