Search Results for author: Georg Hess

Found 10 papers, 7 papers with code

Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap

no code implementations24 Mar 2024 Carl Lindström, Georg Hess, Adam Lilja, Maryam Fatemi, Lars Hammarstrand, Christoffer Petersson, Lennart Svensson

Rather than solely focusing on improving rendering fidelity, we explore simple yet effective methods to enhance perception model robustness to NeRF artifacts without compromising performance on real data.

Autonomous Driving Data Augmentation +2

Transformer-Based Multi-Object Smoothing with Decoupled Data Association and Smoothing

no code implementations22 Dec 2023 Juliano Pinto, Georg Hess, Yuxuan Xia, Henk Wymeersch, Lennart Svensson

Multi-object tracking (MOT) is the task of estimating the state trajectories of an unknown and time-varying number of objects over a certain time window.

Multi-Object Tracking Object

LidarCLIP or: How I Learned to Talk to Point Clouds

1 code implementation13 Dec 2022 Georg Hess, Adam Tonderski, Christoffer Petersson, Kalle Åström, Lennart Svensson

We also explore zero-shot classification and show that LidarCLIP outperforms existing attempts to use CLIP for point clouds by a large margin.

Image Generation Retrieval +1

Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds

1 code implementation1 Jul 2022 Georg Hess, Johan Jaxing, Elias Svensson, David Hagerman, Christoffer Petersson, Lennart Svensson

Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds.

3D Object Detection object-detection +1

Object Detection as Probabilistic Set Prediction

1 code implementation15 Mar 2022 Georg Hess, Christoffer Petersson, Lennart Svensson

Accurate uncertainty estimates are essential for deploying deep object detectors in safety-critical systems.

Object object-detection +1

Can Deep Learning be Applied to Model-Based Multi-Object Tracking?

1 code implementation16 Feb 2022 Juliano Pinto, Georg Hess, William Ljungbergh, Yuxuan Xia, Henk Wymeersch, Lennart Svensson

Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others.

Autonomous Driving Multi-Object Tracking

Deep Deterministic Path Following

no code implementations13 Apr 2021 Georg Hess, William Ljungbergh

This paper deploys the Deep Deterministic Policy Gradient (DDPG) algorithm for longitudinal and lateral control of a simulated car to solve a path following task.

Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning

1 code implementation1 Apr 2021 Juliano Pinto, Georg Hess, William Ljungbergh, Yuxuan Xia, Lennart Svensson, Henk Wymeersch

We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime.

Autonomous Driving

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