We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. Geometric models such as vanishing points, planar homographies or fundamental matrices are essential for 3D scene analysis. Previous approaches discover distinct model instances in an iterative manner, thus limiting their potential for speedup via parallel computation. In contrast, our method detects all model instances independently and in parallel. A neural network segments the input data into clusters representing potential model instances by predicting multiple sets of sample and inlier weights. Using the predicted weights, we determine the model parameters for each potential instance separately in a RANSAC-like fashion. We train the neural network via task-specific loss functions, i.e. we do not require a ground-truth segmentation of the input data. As suitable training data for homography and fundamental matrix fitting is scarce, we additionally present two new synthetic datasets. We demonstrate state-of-the-art performance on these as well as multiple established datasets, with inference times as small as five milliseconds per image.

翻译：我们提出了一种从噪声数据中鲁棒估计多个几何模型实例的实时方法。诸如消失点、平面单应矩阵或基础矩阵等几何模型对于三维场景分析至关重要。现有方法以迭代方式逐个发现不同的模型实例，这限制了其通过并行计算实现加速的潜力。相比之下，我们的方法能够独立且并行地检测所有模型实例。一种神经网络通过预测多组样本权重和内点权重，将输入数据分割为表示潜在模型实例的聚类。基于预测的权重，我们采用类似RANSAC的方式分别确定每个潜在实例的模型参数。该神经网络通过任务特定损失函数进行训练，因此无需输入数据的真实分割标签。针对单应矩阵与基础矩阵拟合训练数据匮乏的问题，我们还额外构建了两个新型合成数据集。实验表明，本方法在以上数据集及多个既有数据集上均实现了最先进性能，且每幅图像的推理时间可低至5毫秒。