DiffSAC用扩散模型优化采样过程，只需评估几十个假设就能达到传统方法数万个假设的效果，大幅提升计算机视觉鲁棒估计效率。
DiffSAC引入扩散模型学习有效最小集分布，通过细化每个数据点置信度而非直接排序，显著减少处理大量劣质集的需求。该方法结合几何特征作为条件约束，仅需评估数十个假设而非传统方法的数万个，在五项经典计算机视觉任务中实现最先进性能。扩散模型的采样加速器使其能够实时运行，可作为即插即用模块改进现有采样共识方法。
DiffSAC: Diffusion-guided Sampling for Consensus-based Robust Estimation
Robust estimation is a core computer vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampling as they struggle to identify effective minimum sets before hypothesis evaluation. To address these challenges, we propose a novel Diffusion-guided Sampling for Consensus-based Robust Estimation (DiffSAC) framework. DiffSAC introduces a diffusion model to learn the distribution of effective minimum sets. It refines the confidence for each data point, indicating whether it belongs to a good minimum set, rather than ranking the data points as in previous work. This significantly reduces the need to process numerous bad sets. To constrain the refinement direction, geometric features are incorporated as conditions within our diffusion model. Consequently, DiffSAC outputs a small number of high-quality minimum sets, enabling identification of the best hypothesis via consensus evaluation. Notably, compared to previous works requiring evaluating over ten thousand hypotheses, DiffSAC achieves state-of-the-art performance with only dozens, significantly boosting efficiency. Extensive experiments across five classic computer vision tasks demonstrate the superiority of DiffSAC. The diffusion model's sampling accelerators enable real-time operation, and DiffSAC can be used as a plug-and-play module to improve existing sample consensus methods.