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20112023
most citedFlowNet: Learning Optical Flow with Convolutional Networks

604 citations · 1.4k across the 83 of their papers we have counts for

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Showing 2018Show all

17 papers · 1 filter

cs.CV2018

Photometric Depth Super-Resolution

Bjoern Haefner, Songyou Peng, Alok Verma +2

This study explores the use of photometric techniques (shape-from-shading and uncalibrated photometric stereo) for upsampling the low-resolution depth map from an RGB-D sensor to t…

cs.CV2018

DeepWrinkles: Accurate and Realistic Clothing Modeling

Zorah Laehner, Daniel Cremers, Tony Tung

We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive com…

cs.CV2018

Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras

Hidenobu Matsuki, Lukas von Stumberg, Vladyslav Usenko +2

We propose a novel real-time direct monocular visual odometry for omnidirectional cameras. Our method extends direct sparse odometry (DSO) by using the unified omnidirectional mode…

cs.CV2018

Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

Lingni Ma, Jörg Stückler, Tao Wu +1

Dense pixelwise prediction such as semantic segmentation is an up-to-date challenge for deep convolutional neural networks (CNNs). Many state-of-the-art approaches either tackle th…

cs.CV2018

LDSO: Direct Sparse Odometry with Loop Closure

Xiang Gao, Rui Wang, Nikolaus Demmel +1

In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO). As a direct…

cs.CV2018

Direct Sparse Odometry with Rolling Shutter

David Schubert, Nikolaus Demmel, Vladyslav Usenko +2

Neglecting the effects of rolling-shutter cameras for visual odometry (VO) severely degrades accuracy and robustness. In this paper, we propose a novel direct monocular VO method t…