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.
no code implementations • 15 Mar 2022 • Christopher Lang, Alexander Braun, Abhinav Valada
Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype.
no code implementations • 21 Dec 2021 • Christopher Lang, Alexander Braun, Abhinav Valada
Object recognition for the most part has been approached as a one-hot problem that treats classes to be discrete and unrelated.