Search Results for author: Cyrill Stachniss

Found 14 papers, 8 papers with code

Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation

no code implementations4 Aug 2021 Felix Stache, Jonas Westheider, Federico Magistri, Marija Popović, Cyrill Stachniss

In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs).

Semantic Segmentation

4D Panoptic LiDAR Segmentation

1 code implementation CVPR 2021 Mehmet Aygün, Aljoša Ošep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens Behley, Laura Leal-Taixé

In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points.

Multi-Object Tracking Scene Understanding +1

Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform

1 code implementation20 Aug 2020 Shijie Li, Xieyuanli Chen, Yun Liu, Dengxin Dai, Cyrill Stachniss, Juergen Gall

Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.

Autonomous Vehicles Real-Time 3D Semantic Segmentation +1

Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching

no code implementations8 Apr 2020 Jan Quenzel, Radu Alexandru Rosu, Thomas Läbe, Cyrill Stachniss, Sven Behnke

We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric.

Image Registration Pose Estimation +2

A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI

no code implementations4 Mar 2020 Jens Behley, Andres Milioto, Cyrill Stachniss

Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.

Instance Segmentation Panoptic Segmentation

ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals

1 code implementation6 May 2019 Emanuele Palazzolo, Jens Behley, Philipp Lottes, Philippe Giguère, Cyrill Stachniss

For localization and mapping, we employ an efficient direct tracking on the truncated signed distance function (TSDF) and leverage color information encoded in the TSDF to estimate the pose of the sensor.

Robotics

Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming

no code implementations9 Jun 2018 Philipp Lottes, Jens Behley, Andres Milioto, Cyrill Stachniss

Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i. e., a semantic segmentation.

Classification General Classification +1

Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

1 code implementation20 Sep 2017 Andres Milioto, Philipp Lottes, Cyrill Stachniss

Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions.

Real-Time Semantic Segmentation

A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration

no code implementations13 Sep 2017 Bartolomeo Della Corte, Igor Bogoslavskyi, Cyrill Stachniss, Giorgio Grisetti

Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.

Point Cloud Registration

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