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Found 7 papers, 1 papers with code
Revisiting Out-of-Distribution Detection in LiDAR-based 3D Object Detection
LiDAR-based 3D object detection has become an essential part of automated driving due to its ability to localize and classify objects precisely in 3D.
Exploiting Sparsity in Automotive Radar Object Detection Networks
This paper investigates sparse convolutional object detection networks, which combine powerful grid-based detection with low compute resources.
Improved Multi-Scale Grid Rendering of Point Clouds for Radar Object Detection Networks
The proposed multi-scale KPPillarsBEV architecture outperforms the baseline by 5. 37% and the previous state of the art by 2. 88% in Car AP4. 0 (average precision for a matching threshold of 4 meters) on the nuScenes validation set.
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
We propose DeepFusion, a modular multi-modal architecture to fuse lidars, cameras and radars in different combinations for 3D object detection.
Self-Supervised Velocity Estimation for Automotive Radar Object Detection Networks
Labels for the Cartesian velocities or contiguous sequences, which are expensive to obtain, are not required.
Improved Orientation Estimation and Detection with Hybrid Object Detection Networks for Automotive Radar
This paper presents novel hybrid architectures that combine grid- and point-based processing to improve the detection performance and orientation estimation of radar-based object detection networks.
DeepReflecs: Deep Learning for Automotive Object Classification with Radar Reflections
The proposed network exploits the specific characteristics of radar reflection data: It handles unordered lists of arbitrary length as input and it combines both extraction of local and global features.
