PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample
  Consensus
Florian Kluger Bodo Rosenhahn
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.