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Found 9 papers, 0 papers with code
Group Regression for Query Based Object Detection and Tracking
We show that the proposed method is applicable to many existing transformer based perception approaches and can bring potential benefits.
Can Transformer Attention Spread Give Insights Into Uncertainty of Detected and Tracked Objects?
Transformers have recently been utilized to perform object detection and tracking in the context of autonomous driving.
Transformers for Object Detection in Large Point Clouds
We present TransLPC, a novel detection model for large point clouds that is based on a transformer architecture.
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.
Transformers for Multi-Object Tracking on Point Clouds
The model utilizes a cross- and a self-attention mechanism and is applicable to lidar data in an automotive context, as well as other data types, such as radar.
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.
Understanding the Domain Gap in LiDAR Object Detection Networks
In order to make autonomous driving a reality, artificial neural networks have to work reliably in the open-world.
Three-dimensional Simultaneous Shape and Pose Estimation for Extended Objects Using Spherical Harmonics
We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object.
