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Found 13 papers, 6 papers with code
Open-World Semantic Segmentation Including Class Similarity
Interpreting camera data is key for autonomously acting systems, such as autonomous vehicles.
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
To address these issues, we propose a novel deep reinforcement learning approach for adaptively replanning robot paths to map targets of interest in unknown 3D environments.
Unsupervised Pre-Training for 3D Leaf Instance Segmentation
Monitoring plants and measuring their traits is an important task in agriculture often referred to as plant phenotyping.
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions
Agricultural production is facing severe challenges in the next decades induced by climate change and the need for sustainability, reducing its impact on the environment.
Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning
We propose a planning method for semi-supervised active learning of semantic segmentation that substantially reduces human labelling requirements compared to fully supervised approaches.
PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
The production of food, feed, fiber, and fuel is a key task of agriculture.
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots
Monitoring plants and fruits at high resolution play a key role in the future of agriculture.
An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping
Our framework combines the mapped acquisition function information into the UAV's planning objectives.
Hierarchical Approach for Joint Semantic, Plant Instance, and Leaf Instance Segmentation in the Agricultural Domain
In this paper, we address the problem of joint semantic, plant instance, and leaf instance segmentation of crop fields from RGB data.
Robust Double-Encoder Network for RGB-D Panoptic Segmentation
We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders.
Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation
Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems.
Adaptive Path Planning for UAV-based Multi-Resolution Semantic Segmentation
In this paper, we address the problem of adaptive path planning for accurate semantic segmentation of terrain using unmanned aerial vehicles (UAVs).
