51 citations · 54 across the 10 of their papers we have counts for
6 papers · 1 filter
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
Georg Hess, Carl Lindström, Maryam Fatemi +2
Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting…
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
Carl Lindström, Georg Hess, Adam Lilja +4
Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research, offering scalable closed-loop simulation and data augmentation capabi…
NeuRAD: Neural Rendering for Autonomous Driving
Adam Tonderski, Carl Lindström, Georg Hess +3
Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of…
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time
Isak Meding, Alexander Bodin, Adam Tonderski +3
Ensembles of independently trained deep neural networks yield uncertainty estimates that rival Bayesian networks in performance. They also offer sizable improvements in terms of pr…
LidarCLIP or: How I Learned to Talk to Point Clouds
Georg Hess, Adam Tonderski, Christoffer Petersson +2
Research connecting text and images has recently seen several breakthroughs, with models like CLIP, DALL-E 2, and Stable Diffusion. However, the connection between text and other v…
Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds
Georg Hess, Johan Jaxing, Elias Svensson +3
Masked autoencoding has become a successful pretraining paradigm for Transformer models for text, images, and, recently, point clouds. Raw automotive datasets are suitable candidat…