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Found 14 papers, 8 papers with code
Video Contrastive Learning with Global Context
Our formulation is able to capture global context in a video, thus robust to temporal content change.
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).
4D Panoptic LiDAR Segmentation
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-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
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.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
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.
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 #18 on
3D Semantic Segmentation
on SemanticKITTI
ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals
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
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 #19 on
3D Semantic Segmentation
on SemanticKITTI
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.
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.
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.
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
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.