Chalmers University of Technology
Whole organizationShowing 10 publications
SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving
LidarCLIP or: How I Learned to Talk to Point Clouds
Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gap
NeuRAD: Neural Rendering for Autonomous Driving
Deep Learning for Model-Based Multi-Object Tracking
Masked Autoencoder for Self-Supervised Pre-Training on Lidar Point Clouds
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Object Detection as Probabilistic Set Prediction
Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
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