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。

确定性几何模型拟合的潜在语义一致性

Latent Semantic Consensus For Deterministic Geometric Model Fitting

We propose a novel method to fit and segment multi-structural data via convex
relaxation. Unlike greedy methods --which maximise the number of inliers-- this
approach efficiently searches for a soft assignment of points to models by
minimising the energy of the overall classification. Our approach is similar to
state-of-the-art energy minimisation techniques which use a global energy.
However, we deal with the scaling factor (as the number of models increases) of
the original combinatorial problem by relaxing the solution. This relaxation
brings two advantages: first, by operating in the continuous domain we can
parallelize the calculations. Second, it allows for the use of different
metrics which results in a more general formulation.
We demonstrate the versatility of our technique on two different problems of
estimating structure from images: plane extraction from RGB-D data and
homography estimation from pairs of images. In both cases, we report accurate
results on publicly available datasets, in most of the cases outperforming the
state-of-the-art.

本文提出了一种通过凸松弛来拟合和分割多结构数据的方法，并演示了该方法在从图像中估算平面提取与单应性估计等问题上的高精度表现。

凸松弛算法进行几何多模型拟合

Geometric Multi-Model Fitting with a Convex Relaxation Algorithm

In this paper, we propose a novel hypergraph based method (called HF) to fit
and segment multi-structural data. The proposed HF formulates the geometric
model fitting problem as a hypergraph partition problem based on a novel
hypergraph model. In the hypergraph model, vertices represent data points and
hyperedges denote model hypotheses. The hypergraph, with large and
"data-determined" degrees of hyperedges, can express the complex relationships
between model hypotheses and data points. In addition, we develop a robust
hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF
can effectively and efficiently estimate the number of, and the parameters of,
model instances in multi-structural data heavily corrupted with outliers
simultaneously. Experimental results show the advantages of the proposed method
over previous methods on both synthetic data and real images.

该论文提出了一种基于超图的方法（称为 HF）来适应和分割多结构数据，该方法采用了一种新的超图模型，可以有效地和高效地估计存在于含有离群值且具有复杂关系的数据中的模型实例的数量和参数，并开发了鲁棒的超图分区算法来检测模型拟合的子超图。