用扩散模型模拟噪声数据，提升学习型RANSAC的泛化能力。
Monte Carlo Diffusion for Generalizable Learning-Based RANSAC
- 通过蒙特卡洛扩散生成多样噪声数据，增强训练鲁棒性。
- 在ScanNet和MegaDepth上显著提升外分布数据的匹配精度。
- 适合需要高鲁棒性的3D重建与视觉定位任务研究者。
随机采样一致性（RANSAC）是基于噪声数据稳健估计参数模型的经典方法。现有学习型RANSAC方法利用深度学习提升对异常值的鲁棒性，但其训练与测试数据均来自相同生成算法，导致推理时面对分布外数据泛化能力有限。为此，本文提出一种基于扩散模型的新范式：通过逐步向真实数据注入噪声，模拟训练所需的噪声条件。为提升数据多样性，引入蒙特卡洛采样，在多个阶段引入不同类型的随机性以逼近多样化数据分布。我们在ScanNet和MegaDepth数据集上进行了特征匹配的全面实验，结果表明，所提出的蒙特卡洛扩散机制显著增强了学习型RANSAC的泛化性能。此外，我们还开展了详尽的消融实验，验证了框架中关键组件的有效性。
原文摘要 · Abstract (English)
Random Sample Consensus (RANSAC) is a fundamental approach for robustly estimating parametric models from noisy data. Existing learning-based RANSAC methods utilize deep learning to enhance the robustness of RANSAC against outliers. However, these approaches are trained and tested on the data generated by the same algorithms, leading to limited generalization to out-of-distribution data during inference. Therefore, in this paper, we introduce a novel diffusion-based paradigm that progressively injects noise into ground-truth data, simulating the noisy conditions for training learning-based RANSAC. To enhance data diversity, we incorporate Monte Carlo sampling into the diffusion paradigm, approximating diverse data distributions by introducing different types of randomness at multiple stages. We evaluate our approach in the context of feature matching through comprehensive experiments on the ScanNet and MegaDepth datasets. The experimental results demonstrate that our Monte Carlo diffusion mechanism significantly improves the generalization ability of learning-based RANSAC. We also develop extensive ablation studies that highlight the effectiveness of key components in our framework.
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