基於蒙特卡羅擴散的可泛化學習型RANSAC

Monte Carlo Diffusion for Generalizable Learning-Based RANSAC

March 12, 2025

作者: Jiale Wang, Chen Zhao, Wei Ke, Tong Zhang

cs.AI

摘要

隨機抽樣一致性（RANSAC）是一種從噪聲數據中穩健估計參數模型的基礎方法。現有的基於學習的RANSAC方法利用深度學習來增強RANSAC對異常值的魯棒性。然而，這些方法在訓練和測試時使用的是由相同算法生成的數據，導致在推理階段對分佈外數據的泛化能力有限。因此，在本文中，我們引入了一種新穎的基於擴散的範式，該範式逐步向真實數據注入噪聲，模擬訓練基於學習的RANSAC時的噪聲條件。為了增強數據多樣性，我們將蒙特卡羅採樣融入擴散範式中，通過在多個階段引入不同類型的隨機性來近似多樣的數據分佈。我們在ScanNet和MegaDepth數據集上通過全面的實驗來評估我們的方法在特徵匹配中的應用。實驗結果表明，我們的蒙特卡羅擴散機制顯著提升了基於學習的RANSAC的泛化能力。我們還進行了廣泛的消融研究，以突出我們框架中關鍵組件的有效性。

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.