Search Results for author:
Found 26 papers, 11 papers with code
On the Reconstruction of Training Data from Group Invariant Networks
Reconstructing training data from trained neural networks is an active area of research with significant implications for privacy and explainability.
Consensus Learning with Deep Sets for Essential Matrix Estimation
Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines.
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.
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.
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.
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.
Point of Care Image Analysis for COVID-19
Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19.
On the Similarity between the Laplace and Neural Tangent Kernels
Experiments show that these kernel methods perform similarly to real neural networks.
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.
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.
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.
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.
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.
Large Scale Correlation Clustering Optimization
This analogy allows us to suggest several new optimization algorithms, which exploit the intrinsic "model-selection" capability of the functional to automatically recover the underlying number of clusters.
