Auto-Rectify Network for Unsupervised Indoor Depth Estimation.

Single-View depth estimation using the CNNs trained from unlabelled videos has shown significant promise. However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices. In this work, we establish that the complex ego-motions exhibited in handheld settings are a critical obstacle for learning depth.

Deterministic Approximate Methods for Maximum Consensus Robust Fitting.

Maximum consensus estimation plays a critically important role in several robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization draw from the class of randomized hypothesize-and-verify algorithms, which are cheap but can usually deliver only rough approximate solutions. On the other extreme, there are exact algorithms which are exhaustive search in nature and can be costly for practical-sized inputs.

Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality.

In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally.

Accelerated Guided Sampling for Multistructure Model Fitting.

The performance of many robust model fitting techniques is largely dependent on the quality of the generated hypotheses. In this paper, we propose a novel guided sampling method, called accelerated guided sampling (AGS), to efficiently generate the accurate hypotheses for multistructure model fitting. Based on the observations that residual sorting can effectively reveal the data relationship (i.

Guaranteed Outlier Removal for Point Cloud Registration with Correspondences.

An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates.

A comprehensive genomic history of extinct and living elephants.

Elephantids are the world's most iconic megafaunal family, yet there is no comprehensive genomic assessment of their relationships. We report a total of 14 genomes, including 2 from the American mastodon, which is an extinct elephantid relative, and 12 spanning all three extant and three extinct elephantid species including an ∼120,000-y-old straight-tusked elephant, a Columbian mammoth, and woolly mammoths. Earlier genetic studies modeled elephantid evolution via simple bifurcating trees, but here we show that interspecies hybridization has been a recurrent feature of elephantid evolution.

Coresets for Triangulation.

Multiple-view triangulation by $\ell _\infty$ minimisation has become established in computer vision. State-of-the-art $\ell _\infty$ triangulation algorithms exploit the quasiconvexity of the cost function to derive iterative update rules that deliver the global minimum. Such algorithms, however, can be computationally costly for large problem instances that contain many image measurements, e.

Clustering with Hypergraphs: The Case for Large Hyperedges.

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of hypergraph clustering, the calculation of such higher order similarities on data subsets gives rise to hyperedges.

Efficient Globally Optimal Consensus Maximisation with Tree Search.

Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising the criterion is still customarily done by randomised sample-and-test techniques, which do not guarantee optimality of the result. Several globally optimal algorithms exist, but they are too slow to challenge the dominance of randomised methods.

Fast Rotation Search with Stereographic Projections for 3D Registration.

Registering two 3D point clouds involves estimating the rigid transform that brings the two point clouds into alignment. Recently there has been a surge of interest in using branch-and-bound (BnB) optimisation for point cloud registration. While BnB guarantees globally optimal solutions, it is usually too slow to be practical.

The Random Cluster Model for Robust Geometric Fitting.

Random hypothesis generation is central to robust geometric model fitting in computer vision. The predominant technique is to randomly sample minimal subsets of the data, and hypothesize the geometric models from the selected subsets. While taking minimal subsets increases the chance of successively "hitting" inliers in a sample, hypotheses fitted on minimal subsets may be severely biased due to the influence of measurement noise, even if the minimal subsets contain purely inliers.

Interacting geometric priors for robust multimodel fitting.

Recent works on multimodel fitting are often formulated as an energy minimization task, where the energy function includes fitting error and regularization terms, such as low-level spatial smoothness and model complexity. In this paper, we introduce a novel energy with high-level geometric priors that consider interactions between geometric models, such that certain preferred model configurations may be induced.We argue that in many applications, such prior geometric properties are available and should be fruitfully exploited.

As-Projective-As-Possible Image Stitching with Moving DLT.

The success of commercial image stitching tools often leads to the impression that image stitching is a "solved problem". The reality, however, is that many tools give unconvincing results when the input photos violate fairly restrictive imaging assumptions; the main two being that the photos correspond to views that differ purely by rotation, or that the imaged scene is effectively planar. Such assumptions underpin the usage of 2D projective transforms or homographies to align photos.

Simultaneously fitting and segmenting multiple-structure data with outliers.

We propose a robust fitting framework, called Adaptive Kernel-Scale Weighted Hypotheses (AKSWH), to segment multiple-structure data even in the presence of a large number of outliers. Our framework contains a novel scale estimator called Iterative Kth Ordered Scale Estimator (IKOSE). IKOSE can accurately estimate the scale of inliers for heavily corrupted multiple-structure data and is of interest by itself since it can be used in other robust estimators.

Accelerated hypothesis generation for multistructure data via preference analysis.

Random hypothesis generation is integral to many robust geometric model fitting techniques. Unfortunately, it is also computationally expensive, especially for higher order geometric models and heavily contaminated data. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting.

Out-of-sample extrapolation of learned manifolds.

We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is still largely unsolved. Taking the perspective of MVU learning being equivalent to Kernel PCA, our problem reduces to extending a kernel matrix generated from an unknown kernel function to novel points.

Incremental kernel principal component analysis.

The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it has exhibited superior performance over previous approaches, such as PCA. However, the standard implementation of KPCA scales badly with the problem size, making computations for large problems infeasible. Also, the "batch" nature of the standard KPCA computation method does not allow for applications that require online processing.