5 papers
FARM: Find Anything using Relational Spatial Memory
Siming He, Leo Huang, Adam Lilja +7
Robots operating in homes, warehouses, and other object-rich environments need memory systems that can find specific object instances on demand. Object-level memory alone is often…
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…
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…
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…