Nicolas Gorlo

2 accepted papers

2026

Describe Anything Anywhere At Any Moment

Nicolas Gorlo, Lukas Schmid, Luca Carlone

CVPR 2026

Computer vision and robotics applications ranging from augmented reality to robot autonomy in large-scale environments require spatio-temporal memory frameworks that capture both geometric structure for accurate language-grounding and semantic detail. Existing methods face a tradeoff, where producin

Cited by 0 Source ↗code ↗Scholar ↗

2024

Long-Term Human Trajectory Prediction Using 3D Dynamic Scene Graphs

Nicolas Gorlo, Lukas Schmid, Luca Carlone

RA-L 2024

We present a novel approach for long-term human trajectory prediction in indoor human-centric environments, which is essential for long-horizon robot planning in these environments. State-of-the-art human trajectory prediction methods are limited by their focus on collision avoidance and short-term

Cited by 20 Source ↗code ↗Scholar ↗