Jens Behley

发表

BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions

C. Stachniss, Federico Magistri, E. Marks, 2023, ArXiv.

Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames

C. Stachniss, C. McCool, J. Behley, 2022, IEEE Robotics and Automation Letters.

High Precision Leaf Instance Segmentation for Phenotyping in Point Clouds Obtained Under Real Field Conditions

C. Stachniss, J. Behley, Rodrigo Marcuzzi, 2023, IEEE Robotics and Automation Letters.

Fruit Tracking Over Time Using High-Precision Point Clouds

C. Stachniss, Maren Bennewitz, Federico Magistri, 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots

C. Stachniss, C. McCool, J. Behley, 2023, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Improving Robotic Fruit Harvesting Within Cluttered Environments Through 3D Shape Completion

C. Stachniss, Federico Magistri, Yue Pan, 2022 .

Towards In-Field Phenotyping Exploiting Differentiable Rendering with Self-Consistency Loss

Cyrill Stachniss, Jens Behley, Nived Chebrolu, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Supplement for In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation

C. Stachniss, Federico Magistri, J. Weyler, 2021 .

SegContrast: 3D Point Cloud Feature Representation Learning Through Self-Supervised Segment Discrimination

C. Stachniss, J. Behley, L. Nunes, 2022, IEEE Robotics and Automation Letters.

Unsupervised Pre-Training for 3D Leaf Instance Segmentation

C. Stachniss, Federico Magistri, Tiziano Guadagnino, 2023, IEEE Robotics and Automation Letters.

Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving

C. Stachniss, J. Behley, Rodrigo Marcuzzi, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Unsupervised Class-Agnostic Instance Segmentation of 3D LiDAR Data for Autonomous Vehicles

C. Stachniss, L. Leal-Taixé, Aljosa Osep, 2022, IEEE Robotics and Automation Letters.

Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions

C. Stachniss, J. Behley, Xieyuanli Chen, 2022, IEEE Robotics and Automation Letters.

Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset

Cyrill Stachniss, Sven Behnke, Jan Quenzel, 2021, Int. J. Robotics Res..

Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks

C. Stachniss, Andres Milioto, J. Behley, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

LiDAR Panoptic Segmentation for Autonomous Driving

C. Stachniss, C. McCool, Andres Milioto, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation

Cyrill Stachniss, Jens Behley, Andres Milioto, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Juergen Gall, C. Stachniss, Sven Behnke, 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Supplement for In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation

C. Stachniss, J. Behley, Federico Magistri, 2021 .

PhenoBench: A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain.

C. Stachniss, Nived Chebrolu, Federico Magistri, 2024, IEEE transactions on pattern analysis and machine intelligence.

Unsupervised Generation of Labeled Training Images for Crop-Weed Segmentation in New Fields and on Different Robotic Platforms

C. Stachniss, Philipp Lottes, J. Weyler, 2023, IEEE Robotics and Automation Letters.

PhenoBench - A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain

C. Stachniss, Nived Chebrolu, J. Behley, 2023, IEEE transactions on pattern analysis and machine intelligence.

On Domain-Specific Pre- Training for Effective Semantic Perception in Agricultural Robotics

G. Grisetti, C. Stachniss, Federico Magistri, 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain

C. Stachniss, J. Behley, Federico Magistri, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain

C. Stachniss, J. Behley, Federico Magistri, 2022, IEEE International Conference on Robotics and Automation.

Open-World Semantic Segmentation Including Class Similarity

C. Stachniss, J. Behley, Federico Magistri, 2024, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Panoptic Segmentation With Partial Annotations for Agricultural Robots

C. Stachniss, J. Behley, T. Läbe, 2024, IEEE Robotics and Automation Letters.

Robust Double-Encoder Network for RGB-D Panoptic Segmentation

C. Stachniss, J. Behley, Federico Magistri, 2022, IEEE International Conference on Robotics and Automation.

Tree Instance Segmentation and Traits Estimation for Forestry Environments Exploiting LiDAR Data Collected by Mobile Robots

C. Stachniss, Matías Mattamala, Nived Chebrolu, 2022 .

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

Cyrill Stachniss, Jens Behley, Philipp Lottes, 2018, IEEE Robotics and Automation Letters.

Joint Plant and Leaf Instance Segmentation on Field-Scale UAV Imagery

C. Stachniss, Philipp Lottes, J. Behley, 2022, IEEE Robotics and Automation Letters.

Retriever: Point Cloud Retrieval in Compressed 3D Maps

C. Stachniss, J. Behley, Rodrigo Marcuzzi, 2022, 2022 International Conference on Robotics and Automation (ICRA).

KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition

C. Stachniss, J. Behley, Louis Wiesmann, 2023, IEEE Robotics and Automation Letters.

Poisson Surface Reconstruction for LiDAR Odometry and Mapping

Cyrill Stachniss, Jens Behley, Nived Chebrolu, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Range Image-based LiDAR Localization for Autonomous Vehicles

Cyrill Stachniss, Jens Behley, Thomas Läbe, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Deep Compression for Dense Point Cloud Maps

Cyrill Stachniss, Xieyuanli Chen, Jens Behley, 2021, IEEE Robotics and Automation Letters.

OverlapNet: Loop Closing for LiDAR-based SLAM

Cyrill Stachniss, Xieyuanli Chen, Jens Behley, 2020, Robotics: Science and Systems.

SuMa++: Efficient LiDAR-based Semantic SLAM

Cyrill Stachniss, Philippe Giguère, Xieyuanli Chen, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

From one field to another - Unsupervised domain adaptation for semantic segmentation in agricultural robotics

C. Stachniss, Philipp Lottes, J. Behley, 2023, Comput. Electron. Agric..

Robust joint stem detection and crop‐weed classification using image sequences for plant‐specific treatment in precision farming

Cyrill Stachniss, Jens Behley, Philipp Lottes, 2019, J. Field Robotics.

Joint Stem Detection and Crop-Weed Classification for Plant-Specific Treatment in Precision Farming

Cyrill Stachniss, Jens Behley, Philipp Lottes, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

C. Stachniss, Andres Milioto, J. Behley, 2021, Autonomous Robots.

Automatic Labeling to Generate Training Data for Online LiDAR-Based Moving Object Segmentation pdf

Cyrill Stachniss, Xieyuanli Chen, Jens Behley, 2022, IEEE Robotics and Automation Letters.

Learning an Overlap-based Observation Model for 3D LiDAR Localization

C. Stachniss, J. Behley, T. Läbe, 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments

Cyrill Stachniss, Jens Behley, C. Stachniss, 2018, Robotics: Science and Systems.

Improving Monocular Depth Estimation by Semantic Pre-training

C. Stachniss, Andres Milioto, J. Behley, 2021, IEEE/RJS International Conference on Intelligent RObots and Systems.

Robust Onboard Localization in Changing Environments Exploiting Text Spotting

C. Stachniss, J. Behley, Louis Wiesmann, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

OverlapNet: a siamese network for computing LiDAR scan similarity with applications to loop closing and localization

C. Stachniss, Andres Milioto, J. Behley, 2021, Autonomous Robots.

Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data

Cyrill Stachniss, Jens Behley, Benedikt Mersch, 2021, IEEE Robotics and Automation Letters.

Joint Plant Instance Detection and Leaf Count Estimation for In-Field Plant Phenotyping

Cyrill Stachniss, Jens Behley, Andres Milioto, 2021, IEEE Robotics and Automation Letters.

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

Cyrill Stachniss, Philippe Giguère, Jens Behley, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations

C. Stachniss, J. Behley, Yue Pan, 2022, IEEE International Conference on Robotics and Automation.

3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation

C. Stachniss, Xingguang Zhong, Yue Pan, 2024, ArXiv.

PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency

C. Stachniss, J. Behley, Louis Wiesmann, 2024, IEEE Transactions on Robotics.

LocNDF: Neural Distance Field Mapping for Robot Localization

C. Stachniss, J. Behley, Yue Pan, 2023, IEEE Robotics and Automation Letters.

VDBFusion: Flexible and Efficient TSDF Integration of Range Sensor Data

C. Stachniss, J. Behley, Tiziano Guadagnino, 2022, Sensors.

KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way

C. Stachniss, J. Behley, Louis Wiesmann, 2022, IEEE Robotics and Automation Letters.

Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion

C. Stachniss, Rodrigo Marcuzzi, Lucas Nunes, 2024, ArXiv.

Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation

C. Stachniss, J. Behley, Xieyuanli Chen, 2023, IEEE Robotics and Automation Letters.

Fast Sparse LiDAR Odometry Using Self-Supervised Feature Selection on Intensity Images

G. Grisetti, C. Stachniss, J. Behley, 2022, IEEE Robotics and Automation Letters.

Adaptive Robust Kernels for Non-Linear Least Squares Problems

Cyrill Stachniss, Jens Behley, Olga Vysotska, 2020, IEEE Robotics and Automation Letters.

Constructing Metric-Semantic Maps Using Floor Plan Priors for Long-Term Indoor Localization

C. Stachniss, Matteo Sodano, Nicky Zimmerman, 2023, 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Long-Term Localization Using Semantic Cues in Floor Plan Maps

C. Stachniss, J. Behley, Xieyuanli Chen, 2022, IEEE Robotics and Automation Letters.

IR-MCL: Implicit Representation-Based Online Global Localization

C. Stachniss, J. Behley, Xieyuanli Chen, 2022, IEEE Robotics and Automation Letters.