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20172022
most citedMonocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss

65 citations · 107 across the 7 of their papers we have counts for

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13 papers · 1 filter

cs.CV2021

BabelCalib: A Universal Approach to Calibrating Central Cameras

Yaroslava Lochman, Kostiantyn Liepieshov, Jianhui Chen +3

Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parame…

cs.CV20211 cited

Escaping Poor Local Minima in Large Scale Robust Estimation

Huu Le, Christopher Zach

Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM). State-of-the-art algorithms for robust estimation, howeve…

cs.CV2020

Progressive Batching for Efficient Non-linear Least Squares

Huu Le, Christopher Zach, Edward Rosten +1

Non-linear least squares solvers are used across a broad range of offline and real-time model fitting problems. Most improvements of the basic Gauss-Newton algorithm tackle converg…

cs.CV2020

A Graduated Filter Method for Large Scale Robust Estimation

Huu Le, Christopher Zach

Due to the highly non-convex nature of large-scale robust parameter estimation, avoiding poor local minima is challenging in real-world applications where input data is contaminate…

cs.CV2019

SG-VAE: Scene Grammar Variational Autoencoder to generate new indoor scenes

Pulak Purkait, Christopher Zach, Ian Reid

Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high-quality images. In this work, we propose a neural network…

cs.CV201965 cited

Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss

Eskil Jörgensen, Christopher Zach, Fredrik Kahl

Three-dimensional object detection from a single view is a challenging task which, if performed with good accuracy, is an important enabler of low-cost mobile robot perception. Pre…