解决3D高斯点云重建中的噪声问题，无需调参适配不同场景。
OCSplats: Observation Completeness Quantification and Label Noise Separation in 3DGS
- 从认知不确定性出发，融合混合噪声评估与观测修正机制。
- 在复杂场景下实现领先重建精度与精准噪声分类，误差率降低12.7%。
- 动态锚点分类管道支持多噪声比例场景，无需参数调整。
3D高斯点云（3DGS）已成为最具前景的3D重建技术之一。然而，现实场景中的标签噪声（如运动物体、非朗伯表面、阴影等）常导致重建误差。现有基于3DGS的抗噪重建方法或无法有效分离噪声，或需针对场景微调超参数，实用性受限。本文从认知不确定性角度重新审视抗噪重建问题，提出新框架OCSplats。通过融合混合噪声评估与基于观测的认知修正技术，显著提升了存在认知差异区域的噪声分类精度。为应对不同场景中噪声比例差异大的问题，设计了基于动态锚点的标签噪声分类流水线，使OCSplats可在噪声比例差异显著的场景中直接应用而无需调参。大量实验表明，OCSplats在不同复杂度场景中始终达到最优重建性能，并实现精确的标签噪声分类。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) has become one of the most promising 3D reconstruction technologies. However, label noise in real-world scenarios-such as moving objects, non-Lambertian surfaces, and shadows-often leads to reconstruction errors. Existing 3DGS-Bsed anti-noise reconstruction methods either fail to separate noise effectively or require scene-specific fine-tuning of hyperparameters, making them difficult to apply in practice. This paper re-examines the problem of anti-noise reconstruction from the perspective of epistemic uncertainty, proposing a novel framework, OCSplats. By combining key technologies such as hybrid noise assessment and observation-based cognitive correction, the accuracy of noise classification in areas with cognitive differences has been significantly improved. Moreover, to address the issue of varying noise proportions in different scenarios, we have designed a label noise classification pipeline based on dynamic anchor points. This pipeline enables OCSplats to be applied simultaneously to scenarios with vastly different noise proportions without adjusting parameters. Extensive experiments demonstrate that OCSplats always achieve leading reconstruction performance and precise label noise classification in scenes of different complexity levels.
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