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Found 9 papers, 4 papers with code
A Benchmark for LiDAR-based Panoptic Segmentation based on KITTI
Panoptic segmentation is the recently introduced task that tackles semantic segmentation and instance segmentation jointly.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #19 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
Detection of Single Grapevine Berries in Images Using Fully Convolutional Neural Networks
We count single berries in images to avoid the error-prone detection of grapevine clusters.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on
3D Semantic Segmentation
on SemanticKITTI
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
It outputs the stem location for weeds, which allows for mechanical treatments, and the covered area of the weed for selective spraying.
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
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.
Ocean Eddy Identification and Tracking using Neural Networks
We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.
Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs
We provide an open-source codebase for training and deployment.
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
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.
