no code implementations • 6 Mar 2023 • Monish R. Nallapareddy, Kshitij Sirohi, Paulo L. J. Drews-Jr, Wolfram Burgard, Chih-Hong Cheng, Abhinav Valada
In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties.
1 code implementation • 10 Oct 2022 • Kshitij Sirohi, Sajad Marvi, Daniel Büscher, Wolfram Burgard
Current learning-based methods typically try to achieve maximum performance for this task, while neglecting a proper estimation of the associated uncertainties.
1 code implementation • 29 Jun 2022 • Kshitij Sirohi, Sajad Marvi, Daniel Büscher, Wolfram Burgard
In this work, we introduce the novel task of uncertainty-aware panoptic segmentation, which aims to predict per-pixel semantic and instance segmentations, together with per-pixel uncertainty estimates.
no code implementations • 20 Oct 2021 • Kürsat Petek, Kshitij Sirohi, Daniel Büscher, Wolfram Burgard
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a challenging topic in research.
no code implementations • 16 Feb 2021 • Kshitij Sirohi, Rohit Mohan, Daniel Büscher, Wolfram Burgard, Abhinav Valada
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors.