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Found 38 papers, 14 papers with code
RESFM: Robust Equivariant Multiview Structure from Motion
Multiview Structure from Motion is a fundamental and challenging computer vision problem.
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum
We leverage the duality between wide neural networks and Neural Tangent Kernels and propose a preconditioned gradient descent method, which alters the trajectory of GD.
Leveraging Image Matching Toward End-to-End Relative Camera Pose Regression
This paper proposes a generalizable, end-to-end deep learning-based method for relative pose regression between two images.
A Kernel Perspective of Skip Connections in Convolutional Networks
Over-parameterized residual networks (ResNets) are amongst the most successful convolutional neural architectures for image processing.
On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels
We study the properties of various over-parametrized convolutional neural architectures through their respective Gaussian process and neural tangent kernels.
Deep Permutation Equivariant Structure from Motion
Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i. e., when cameras are both internally and externally calibrated.
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks
Deep residual network architectures have been shown to achieve superior accuracy over classical feed-forward networks, yet their success is still not fully understood.
Shift Invariance Can Reduce Adversarial Robustness
Using this, we prove that shift invariance in neural networks produces adversarial examples for the simple case of two classes, each consisting of a single image with a black or white dot on a gray background.
Segmenting Microcalcifications in Mammograms and its Applications
Microcalcifications are small deposits of calcium that appear in mammograms as bright white specks on the soft tissue background of the breast.
On the Similarity between the Laplace and Neural Tangent Kernels
Experiments show that these kernel methods perform similarly to real neural networks.
The Trimmed Lasso: Sparse Recovery Guarantees and Practical Optimization by the Generalized Soft-Min Penalty
We present a new approach to solve the sparse approximation or best subset selection problem, namely find a $k$-sparse vector ${\bf x}\in\mathbb{R}^d$ that minimizes the $\ell_2$ residual $\lVert A{\bf x}-{\bf y} \rVert_2$.
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance
In this work we address the challenging problem of multiview 3D surface reconstruction.
Learning Algebraic Multigrid Using Graph Neural Networks
Efficient numerical solvers for sparse linear systems are crucial in science and engineering.
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Recent works have partly attributed the generalization ability of over-parameterized neural networks to frequency bias -- networks trained with gradient descent on data drawn from a uniform distribution find a low frequency fit before high frequency ones.
Averaging Essential and Fundamental Matrices in Collinear Camera Settings
Global methods to Structure from Motion have gained popularity in recent years.
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
We study the relationship between the frequency of a function and the speed at which a neural network learns it.
Algebraic Characterization of Essential Matrices and Their Averaging in Multiview Settings
A common approach to essential matrix averaging is to separately solve for camera orientations and subsequently for camera positions.
Learning to Optimize Multigrid PDE Solvers
Constructing fast numerical solvers for partial differential equations (PDEs) is crucial for many scientific disciplines.
Resultant Based Incremental Recovery of Camera Pose from Pairwise Matches
In this paper, we introduce a novel solution to the six-point online algorithm to recover the exterior parameters associated with $I_n$.
GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices
First, given ${n \choose 2}$ fundamental matrices computed for $n$ images, we provide a complete algebraic characterization in the form of conditions that are both necessary and sufficient to enabling the recovery of camera matrices.
SpectralNet: Spectral Clustering using Deep Neural Networks
Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points.
Photometric Stereo by Hemispherical Metric Embedding
We present a novel embedding method that maps pixels to normals on the unit hemisphere.
On Detection of Faint Edges in Noisy Images
A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected.
A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery
We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6.
Solving Uncalibrated Photometric Stereo Using Fewer Images by Jointly Optimizing Low-rank Matrix Completion and Integrability
We introduce a new, integrated approach to uncalibrated photometric stereo.
A Survey of Structure from Motion
The structure from motion (SfM) problem in computer vision is the problem of recovering the three-dimensional ($3$D) structure of a stationary scene from a set of projective measurements, represented as a collection of two-dimensional ($2$D) images, via estimation of motion of the cameras corresponding to these images.
Efficient Representation of Low-Dimensional Manifolds using Deep Networks
We consider the ability of deep neural networks to represent data that lies near a low-dimensional manifold in a high-dimensional space.
A Multiscale Variable-Grouping Framework for MRF Energy Minimization
We present a multiscale approach for minimizing the energy associated with Markov Random Fields (MRFs) with energy functions that include arbitrary pairwise potentials.
Wide Baseline Stereo Matching With Convex Bounded Distortion Constraints
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.
Elasticity-based Matching by Minimizing the Symmetric Difference of Shapes
Using linearized elasticity theory and the finite element method we seek an elastic deformation that is caused by simple external boundary forces and accounts for the difference between the two shapes.
Learning 3D Deformation of Animals from 2D Images
In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal.
A Hyperelastic Two-Scale Optimization Model for Shape Matching
We suggest a novel shape matching algorithm for three-dimensional surface meshes of disk or sphere topology.
Wide baseline stereo matching with convex bounded-distortion constraints
This paper focuses on the challenging problem of finding correspondences once approximate epipolar constraints are given.
Fast Detection of Curved Edges at Low SNR
Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images.
A Global Approach for Solving Edge-Matching Puzzles
We consider apictorial edge-matching puzzles, in which the goal is to arrange a collection of puzzle pieces with colored edges so that the colors match along the edges of adjacent pieces.
Stable Camera Motion Estimation Using Convex Programming
We further identify the implications of parallel rigidity theory for the location estimation problem to be well-posed, and prove exact (in the noiseless case) and stable location recovery results.
From Shading to Local Shape
We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch.
Single View Depth Estimation from Examples
The known depths of the selected database objects act as shape priors which constrain the process of estimating the object's depth.
