Estimating reliable geometric model parameters from the data with severe
outliers is a fundamental and important task in computer vision. This paper
attempts to sample high-quality subsets and select model instances to estimate
parameters in the multi-structural data. To address this, we propose an
effective method called Latent Semantic Consensus (LSC). The principle of LSC
is to preserve the latent semantic consensus in both data points and model
hypotheses. Specifically, LSC formulates the model fitting problem into two
latent semantic spaces based on data points and model hypotheses, respectively.
Then, LSC explores the distributions of points in the two latent semantic
spaces, to remove outliers, generate high-quality model hypotheses, and
effectively estimate model instances. Finally, LSC is able to provide
consistent and reliable solutions within only a few milliseconds for general
multi-structural model fitting, due to its deterministic fitting nature and
efficiency. Compared with several state-of-the-art model fitting methods, our
LSC achieves significant superiority for the performance of both accuracy and
speed on synthetic data and real images. The code will be available at
this https URL

该论文提出了一种名为潜在语义共识（LSC）的有效方法，用于从存在严重离群值的数据中提取可靠的几何模型参数，并实现了高精度和高效的模型拟合。