Nikolaus Mayer — accepted AI-conference papers

2019

Automated Boxwood Topiary Trimming with a Robotic Arm and Integrated Stereo Vision

Dejan Kaljaca, Nikolaus Mayer, Bastiaan Vroegindeweij, Angelo Mencarelli, Eldert van Henten, Thomas Brox

IROS 2019poster

This paper presents an integrated hardware-software solution to perform fully automated robotic bush trimming to user-specified shapes. In contrast to specialized solutions that can trim only bushes of a certain shape, the approach ensures flexibility via a vision-based shape fitting module that all…

Cited by 15 Source ↗Scholar ↗

2017

DeMoN: Depth and Motion Network for Learning Monocular Stereo

Benjamin Ummenhofer, Huizhong Zhou, Jonas Uhrig, Nikolaus Mayer, Eddy Ilg, Alexey Dosovitskiy +1

CVPR 2017poster

In this paper we formulate structure from motion as a learning problem. We train a convolutional network end-to-end to compute depth and camera motion from successive, unconstrained image pairs. The architecture is composed of multiple stacked encoder-decoder networks, the core part being an iterati…

Cited by 880 PDF ↗Scholar ↗

2017

FlowNet 2.0: Evolution of Optical Flow Estimation With Deep Networks

Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, Thomas Brox

CVPR 2017poster

The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variationa…

Cited by 4066 PDF ↗Scholar ↗

2016

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation

Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy +1

CVPR 2016poster

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so-called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical f…

Cited by 3436 PDF ↗Scholar ↗