13 citations · 14 across the 8 of their papers we have counts for
10 papers
Differentiable Voronoi Ray Tracing Beyond Rasterization Speeds
Bernardo Taveira, Carl Lindström, Joakim Johnander +1
Real-time novel view synthesis is dominated by rasterized explicit primitives. These projection-based pipelines provide high throughput but require specialized extensions for non-p…
SwInception -- Local Attention Meets Convolutions
David Hagerman, Roman Naeem, Jakob Lindqvist +3
Sparse vision transformers have gained popularity as efficient encoders for medical volumetric segmentation, with Swin emerging as a prominent choice. Swin uses local attention to…
Scalable GPU Construction of 3D Voronoi and Power Diagrams
Bernardo Taveira, Carl Lindström, Maryam Fatemi +2
Voronoi diagrams, and their more general weighted counterpart, power diagrams, are fundamental geometric constructs with wide-ranging applications. Recently, they have gained renew…
IDSplat: Instance-Decomposed 3D Gaussian Splatting for Driving Scenes
Carl Lindström, Mahan Rafidashti, Maryam Fatemi +3
Reconstructing dynamic driving scenes is essential for developing autonomous systems through sensor-realistic simulation. Although recent methods achieve high-fidelity reconstructi…
MTGS: Multi-Traversal Gaussian Splatting
Tianyu Li, Yihang Qiu, Zhenhua Wu +4
Multi-traversal data, commonly collected through daily commutes or by self-driving fleets, provides multiple viewpoints for scene reconstruction within a road block. This data offe…
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling +6
Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance…
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…
Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving
Mina Alibeigi, William Ljungbergh, Adam Tonderski +7
Existing datasets for autonomous driving (AD) often lack diversity and long-range capabilities, focusing instead on 360° perception and temporal reasoning. To address this gap, we…