activity
20162020
most citedHyperSTAR: Task-Aware Hyperparameters for Deep Networks

4 citations · 5 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2020

Efficient Scene Compression for Visual-based Localization

Marcela Mera-Trujillo, Benjamin Smith, Victor Fragoso

Estimating the pose of a camera with respect to a 3D reconstruction or scene representation is a crucial step for many mixed reality and robotics applications. Given the vast amoun…

cs.CV2020

Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints

Victor Fragoso, Sudipta Sinha

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs. Unlike most pose-…

cs.CV20204 cited

HyperSTAR: Task-Aware Hyperparameters for Deep Networks

Gaurav Mittal, Chang Liu, Nikolaos Karianakis +3

While deep neural networks excel in solving visual recognition tasks, they require significant effort to find hyperparameters that make them work optimally. Hyperparameter Optimiza…

cs.CV2020

gDLS*: Generalized Pose-and-Scale Estimation Given Scale and Gravity Priors

Victor Fragoso, Joseph DeGol, Gang Hua

Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured…

cs.CV20171 cited

GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion

Qiaodong Cui, Victor Fragoso, Chris Sweeney +1

We present GraphMatch, an approximate yet efficient method for building the matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike modern SfM pipelines that u…

cs.CV2017

ANSAC: Adaptive Non-minimal Sample and Consensus

Victor Fragoso, Chris Sweeney, Pradeep Sen +1

While RANSAC-based methods are robust to incorrect image correspondences (outliers), their hypothesis generators are not robust to correct image correspondences (inliers) with posi…