116 citations · 170 across the 11 of their papers we have counts for
12 papers
Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation
Ken Deng, Yifu Qiu, Yoni Kasten +2
We study whether vision-language models (VLMs) can solve relative camera pose estimation (RCPE) from image pairs, a direct test of multi-view spatial reasoning. We cast RCPE as a d…
GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations
Fadi Khatib, Dror Moran, Guy Trostianetsky +3
We introduce GSVisLoc, a visual localization method designed for 3D Gaussian Splatting (3DGS) scene representations. Given a 3DGS model of a scene and a query image, our goal is to…
RESfM: Robust Deep Equivariant Structure from Motion
Fadi Khatib, Yoni Kasten, Dror Moran +2
Multiview Structure from Motion is a fundamental and challenging computer vision problem. A recent deep-based approach utilized matrix equivariant architectures for simultaneous re…
Volume Rendering of Neural Implicit Surfaces
Lior Yariv, Jiatao Gu, Yoni Kasten +1
Neural volume rendering became increasingly popular recently due to its success in synthesizing novel views of a scene from a sparse set of input images. So far, the geometry learn…
Deep Permutation Equivariant Structure from Motion
Dror Moran, Hodaya Koslowsky, Yoni Kasten +3
Existing deep methods produce highly accurate 3D reconstructions in stereo and multiview stereo settings, i.e., when cameras are both internally and externally calibrated. Neverthe…
On the Similarity between the Laplace and Neural Tangent Kernels
Amnon Geifman, Abhay Yadav, Yoni Kasten +3
Recent theoretical work has shown that massively overparameterized neural networks are equivalent to kernel regressors that use Neural Tangent Kernels(NTK). Experiments show that t…
Frequency Bias in Neural Networks for Input of Non-Uniform Density
Ronen Basri, Meirav Galun, Amnon Geifman +3
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…
Averaging Essential and Fundamental Matrices in Collinear Camera Settings
Amnon Geifman, Yoni Kasten, Meirav Galun +1
Global methods to Structure from Motion have gained popularity in recent years. A significant drawback of global methods is their sensitivity to collinear camera settings. In this…
The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies
Ronen Basri, David Jacobs, Yoni Kasten +1
We study the relationship between the frequency of a function and the speed at which a neural network learns it. We build on recent results that show that the dynamics of overparam…
Algebraic Characterization of Essential Matrices and Their Averaging in Multiview Settings
Yoni Kasten, Amnon Geifman, Meirav Galun +1
Essential matrix averaging, i.e., the task of recovering camera locations and orientations in calibrated, multiview settings, is a first step in global approaches to Euclidean stru…
Resultant Based Incremental Recovery of Camera Pose from Pairwise Matches
Yoni Kasten, Meirav Galun, Ronen Basri
Incremental (online) structure from motion pipelines seek to recover the camera matrix associated with an image given images, , whose camera matrices h…
GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices
Yoni Kasten, Amnon Geifman, Meirav Galun +1
This paper addresses the problem of recovering projective camera matrices from collections of fundamental matrices in multiview settings. We make two main contributions. First, giv…