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06/08/2023
Unscented Autoencoder
The Variational Autoencoder (VAE) is a seminal approach in deep generati...
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09/26/2022
DeepFusion: A Robust and Modular 3D Object Detector for Lidars, Cameras and Radars
We propose DeepFusion, a modular multi-modal architecture to fuse lidars...
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12/18/2020
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
The availability of many real-world driving datasets is a key reason beh...
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08/10/2020
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Reliable uncertainty estimation is crucial for robust object detection i...
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03/07/2020
Inferring Spatial Uncertainty in Object Detection
The availability of real-world datasets is the prerequisite to develop o...
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02/01/2020
Leveraging Uncertainties for Deep Multi-modal Object Detection in Autonomous Driving
This work presents a probabilistic deep neural network that combines LiD...
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09/26/2019
Can We Trust You? On Calibration of a Probabilistic Object Detector for Autonomous Driving
Reliable uncertainty estimation is crucial for perception systems in saf...
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07/16/2019
Learning Multimodal Fixed-Point Weights using Gradient Descent
Due to their high computational complexity, deep neural networks are sti...
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02/21/2019
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges
Recent advancements in the perception for autonomous driving are driven ...
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01/29/2019
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Training a deep object detector for autonomous driving requires a huge a...
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09/14/2018
Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection
We present a robust real-time LiDAR 3D object detector that leverages he...
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04/13/2018