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Found 6 papers, 6 papers with code
Uncertainty-Aware AB3DMOT by Variational 3D Object Detection
Autonomous driving needs to rely on high-quality 3D object detection to ensure safe navigation in the world.
Variational Voxel Pseudo Image Tracking
Uncertainty estimation is an important task for critical problems, such as robotics and autonomous driving, because it allows creating statistically better perception models and signaling the model's certainty in its predictions to the decision method or a human supervisor.
Layer Ensembles
Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions.
Variational Neural Networks
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertainty of a neural network by considering a distribution over weights and sampling different models for each input.
VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images
In this paper, we propose a novel voxel-based 3D single object tracking (3D SOT) method called Voxel Pseudo Image Tracking (VPIT).
Analysis of voxel-based 3D object detection methods efficiency for real-time embedded systems
This means that the methods can achieve a speed-up of $40$-$60\%$ by restricting operation to near objects while not sacrificing much in performance.
