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Found 7 papers, 2 papers with code
Cross-Dataset Experimental Study of Radar-Camera Fusion in Bird's-Eye View
By exploiting complementary sensor information, radar and camera fusion systems have the potential to provide a highly robust and reliable perception system for advanced driver assistance systems and automated driving functions.
RC-BEVFusion: A Plug-In Module for Radar-Camera Bird's Eye View Feature Fusion
Radars and cameras belong to the most frequently used sensors for advanced driver assistance systems and automated driving research.
Deployment of Deep Neural Networks for Object Detection on Edge AI Devices with Runtime Optimization
We therefore perform a case study of the deployment of two representative object detection networks on an edge AI platform.
MASS: Multi-Attentional Semantic Segmentation of LiDAR Data for Dense Top-View Understanding
At the heart of all automated driving systems is the ability to sense the surroundings, e. g., through semantic segmentation of LiDAR sequences, which experienced a remarkable progress due to the release of large datasets such as SemanticKITTI and nuScenes-LidarSeg.
PillarSegNet: Pillar-based Semantic Grid Map Estimation using Sparse LiDAR Data
The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios.
SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation
Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information.
TARGER: Neural Argument Mining at Your Fingertips
We present TARGER, an open source neural argument mining framework for tagging arguments in free input texts and for keyword-based retrieval of arguments from an argument-tagged web-scale corpus.
