no code implementations • 19 Apr 2024 • Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
no code implementations • 25 Apr 2023 • Christopher Lang, Alexander Braun, Lars Schillingmann, Abhinav Valada
We hypothesize that this drawback results from formulating self-supervised objectives that are limited to single frames or frame pairs.
no code implementations • 17 Feb 2023 • Christopher Lang, Alexander Braun, Lars Schillingmann, Karsten Haug, Abhinav Valada
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide.