Spatially Coherent RANSAC for Multi-Model Fitting

RANSAC [15, 38, 1] is a reliable method for fitting parametric models to sparse data with many outliers. Originally designed for extracting a single model, RANSAC also has variants for fitting multiple models when supported by data. Our main insight is that, in practice, inliers for each model are often spatially coherent — all previous RANSAC-based methods ignore this. Our new method fits an unspecified number of models to data by combining ideas of random sampling and spatial regularization. As in basic RANSAC, we randomly sample data points to generate a set of proposed models (labels). We formulate model selection and inlier classification as a single problem — labeling of triangulated data points. Geometric fit errors and spatial coherence are combined in one MRF-based energy. In contrast to basic RANSAC, inlier classification does not depend on a fixed threshold. Moreover, our optimization framework allows iterative re-estimation of models/inliers with a clear stopping criteria and convergence guarantees. We show that our new method, SCO- RANSAC, can significantly improve results on synthetic and real data supporting multiple linear, affine, and homographic models

Coplanar Repeats by Energy Minimization

This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization. The proposed energy functional combines several features that model how planes with coplanar repeats are projected into images and captures global interactions between different coplanar repeat groups and scene planes. An inference framework based on a recent variant of $\alpha$-expansion is described and fast convergence is demonstrated. We compare the proposed method to two widely-used geometric multi-model fitting methods using a new dataset of annotated images containing multiple scene planes with coplanar repeats in varied arrangements. The evaluation shows a significant improvement in the accuracy of rectifications computed from coplanar repeats detected with the proposed method versus those detected with the baseline methods.Comment: 14 pages with supplemental materials attache

Labeled Sampling Consensus A Novel Algorithm For Robustly Fitting Multiple Structures Using Compressed Sampling

The ability to robustly fit structures in datasets that contain outliers is a very important task in Image Processing, Pattern Recognition and Computer Vision. Random Sampling Consensus or RANSAC is a very popular method for this task, due to its ability to handle over 50% outliers. The problem with RANSAC is that it is only capable of finding a single structure. Therefore, if a dataset contains multiple structures, they must be found sequentially by finding the best fit, removing the points, and repeating the process. However, removing incorrect points from the dataset could prove disastrous. This thesis offers a novel approach to sampling consensus that extends its ability to discover multiple structures in a single iteration through the dataset. The process introduced is an unsupervised method, requiring no previous knowledge to the distribution of the input data. It uniquely assigns labels to different instances of similar structures. The algorithm is thus called Labeled Sampling Consensus or L-SAC. These unique instances will tend to cluster around one another allowing the individual structures to be extracted using simple clustering techniques. Since divisions instead of modes are analyzed, only a single instance of a structure need be recovered. This ability of L-SAC allows a novel sampling procedure to be presented “compressing” the required samples needed compared to traditional sampling schemes while ensuring all structures have been found. L-SAC is a flexible framework that can be applied to many problem domains

Motion segmentation based robust RGB-D SLAM

© 2014 IEEE. A sparse feature-based motion segmentation algorithm for RGB-D data is proposed which offers us a unified way to handle outliers and dynamic scenarios. Together with the pose-graph SLAM framework, they constitute an effective and robust solution that enable us to do RGB-D SLAM in wide range of situations, although traditionally they have been divided into different categories and treated separately using different kinds of methods. Through comparisons with RANSAC using simulated data and testing with different benchmark RGB-D datasets against the state-of-the-art method in RGB-D SLAM, we show that our solution is efficient and effective in handling general static and dynamic scenarios, some of which have not be achieved before