1 citations · 1 across the 6 of their papers we have counts for
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QueryOcc: Query-based Self-Supervision for 3D Semantic Occupancy
Adam Lilja, Ji Lan, Junsheng Fu +1
Learning 3D scene geometry and semantics from images is a core challenge in computer vision and a key capability for autonomous driving. Since large-scale 3D annotation is prohibit…
Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping
Chouaib Bencheikh Lehocine, Adam Lilja, Junsheng Fu +1
Online map estimation is a crucial component of autonomous driving systems that reduces the reliance on costly high-definition maps. State-of-the-art (SOTA) methods commonly predic…
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…
Semi-Supervised Hierarchical Open-Set Classification
Erik Wallin, Fredrik Kahl, Lars Hammarstrand
Hierarchical open-set classification handles previously unseen classes by assigning them to the most appropriate high-level category in a class taxonomy. We extend this paradigm to…
NeuRadar: Neural Radiance Fields for Automotive Radar Point Clouds
Mahan Rafidashti, Ji Lan, Maryam Fatemi +3
Radar is an important sensor for autonomous driving (AD) systems due to its robustness to adverse weather and different lighting conditions. Novel view synthesis using neural radia…
Exploring Semi-Supervised Learning for Online Mapping
Adam Lilja, Erik Wallin, Junsheng Fu +1
The ability to generate online maps using only onboard sensory information is crucial for enabling autonomous driving beyond well-mapped areas. Training models for this task -- pre…