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20112022
most citedFlowNet: Learning Optical Flow with Convolutional Networks

604 citations · 1.4k across the 64 of their papers we have counts for

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74 papers · 1 filter

cs.CV2022

G-MSM: Unsupervised Multi-Shape Matching with Graph-based Affinity Priors

Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +1

We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised learning approach for non-rigid shape correspondence. Rather than treating a collection of input poses as…

cs.CV20221 cited

High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF

Lu Sang, Bjoern Haefner, Xingxing Zuo +1

Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to re…

cs.CV2022

DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment

Mariia Gladkova, Nikita Korobov, Nikolaus Demmel +3

Direct methods have shown excellent performance in the applications of visual odometry and SLAM. In this work we propose to leverage their effectiveness for the task of 3D multi-ob…

cs.CV20229 cited

VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments

Michael Schleiss, Fahmi Rouatbi, Daniel Cremers

Visual Place Recognition and Visual Localization are essential components in navigation and mapping for autonomous vehicles especially in GNSS-denied navigation scenarios. Recent w…

cs.CV20222 cited

A Unified Framework for Implicit Sinkhorn Differentiation

Marvin Eisenberger, Aysim Toker, Laura Leal-Taixé +2

The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields. One major reason is its ease of integration into deep learning framework…

cs.CV20221 cited

A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching

Paul Roetzer, Paul Swoboda, Daniel Cremers +1

We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant forma…