Interacting Geometric Priors For Robust Multi-Model Fitting: Supplementary Material

نویسندگان

  • Trung T. Pham
  • David Suter
چکیده

where l is a positive spatial smoothness penalty, G(fp, fq) is a function measuring the geometric inconsistency between fp and fq . Under the condition that l ≥ G(., .) ≥ 0, function (6) is submodular, and can be minimised by using graph-cut based methods (e.g., PEARL [1]). However, the condition l ≥ G(., .) ≥ 0 clearly limits the effect of the geometric consistency since the penalty G(., .) is bounded by l. Indeed, we have tried PEARL with the piecewise smooth function (6), yet the benefit of the geometric consistency is diminishing. More importantly, in the applications where the smoothness assumption is not valid (e.g., vanishing point detection), it is impossible to penalise the geometric inconsistency using (6) because the smoothness cost is set to zero (i.e., l = 0). III. PLANAR SURFACE RECONSTRUCTION FROM 3D POINT CLOUDS.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Bayesian Sample size Determination for Longitudinal Studies with Continuous Response using Marginal Models

Introduction Longitudinal study designs are common in a lot of scientific researches, especially in medical, social and economic sciences. The reason is that longitudinal studies allow researchers to measure changes of each individual over time and often have higher statistical power than cross-sectional studies. Choosing an appropriate sample size is a crucial step in a successful study. A st...

متن کامل

Energy Based Multi-model Fitting & Matching for 3D Reconstruction

Standard geometric model fitting methods take as an input a fixed set of feature pairs greedily matched based only on their appearances. Inadvertently, many valid matches are discarded due to repetitive texture or large baseline between view points. To address this problem, matching should consider both feature appearances and geometric fitting errors. We jointly solve feature matching and mult...

متن کامل

Geometric Shrinkage Priors for Kählerian Signal Filters

We construct geometric shrinkage priors for Kählerian signal filters. Based on the characteristics of Kähler manifold, an algorithm for finding the superharmonic priors is introduced. The algorithm is efficient and robust to obtain the Komaki priors. Several ansätze for the priors are also suggested. In particular, the ansätze related to Kähler potential are geometrically intrinsic priors to th...

متن کامل

Effective Sampling: Fast Segmentation Using Robust Geometric Model Fitting

Identifying the underlying models in a set of data points contaminated by noise and outliers, leads to a highly complex multi-model fitting problem. This problem can be posed as a clustering problem by the projection of higher order affinities between data points into a graph, which can then be clustered using spectral clustering. Calculating all possible higher order affinities is computationa...

متن کامل

Hypergraph modelling for geometric model fitting

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 ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014