Scientific contributions

FlowNet 2.0 @CVPR 2017

FlowNet 2.0 is the first optical flow approach based on deep learning that reaches state-of-the-art accuracy. At the same time it is by a factor 100 faster than previous state-of-the-art techniques. This allows for reliable motion estimation at interactive frame rates. For more information visit the paper page.

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DeMoN @CVPR 2017

DeMoN is the very first work that formulates the problem of joint egomotion and depth estimation as a pure learning problem. Given two images from a single moving camera, DeMoN can estimate depth and camera motion at interactive frame rates. For more information, please visit the website.

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Twitter
RT @DAgriFood: 🛰🚜🌾 EU’s future cyber-farms to utilise #drones, #robots and sensors ▶️ https://t.co/99rzcR0D4H @HorizonMagEU @IFParis @IoF20…1 month
#Pictures from the last consortium meeting in Renningen #robotics #trimbot #H2020 @EU_H2020 @RoboticsEU https://t.co/6uRcXqth7M3 months
TrimBot2020's robotic arm cuts bushes #robotics #automatic #cutting #bush #gardening #research #sterevision… https://t.co/SoWhNc4SXm3 months
@Jesse_Scholtes @farmtechnology @WUR @Jesse_Scholtes look at the demo video of the cutting https://t.co/LowIj2NwpA3 months
Automatic cutting of bushes with the roboti arm of TrimBot: https://t.co/fW6lnAc1Ew via @YouTube4 months