no code implementations • 10 May 2024 • MingYu Liu, Ekim Yurtsever, Marc Brede, Jun Meng, Walter Zimmer, Xingcheng Zhou, Bare Luka Zagar, Yuning Cui, Alois Knoll
In this study, we introduce an object relation module, consisting of a graph generator and a graph neural network (GNN), to learn the spatial information from certain patterns to improve 3D object detection.
no code implementations • 2 May 2024 • Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou, MingYu Liu, Alois C. Knoll
We achieve a frame rate of 10 FPS while keeping compression sizes below 105 Kb, a reduction of 50 times, and maintaining object detection performance on par with the original data.
3 code implementations • 2 Mar 2024 • Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan, Xingcheng Zhou, Rui Song, Alois C. Knoll
We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task.
no code implementations • 12 Feb 2024 • Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll
Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation.
1 code implementation • 5 Feb 2024 • Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer, Christian Creß, Andreas Møgelmose, Mohan M. Trivedi, Alois C. Knoll
We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training.
no code implementations • 16 Jan 2024 • Christian Creß, Walter Zimmer, Nils Purschke, Bach Ngoc Doan, Sven Kirchner, Venkatnarayanan Lakshminarasimhan, Leah Strand, Alois C. Knoll
To the best of our knowledge, no targetless calibration between event-based and RGB cameras can handle multiple moving objects, nor does data fusion optimized for the domain of roadside ITS exist.
2 code implementations • 2 Jan 2024 • MingYu Liu, Ekim Yurtsever, Jonathan Fossaert, Xingcheng Zhou, Walter Zimmer, Yuning Cui, Bare Luka Zagar, Alois C. Knoll
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques.
no code implementations • 2 Nov 2023 • Xinyi Li, Zijian Ma, Yinlong Liu, Walter Zimmer, Hu Cao, Feihu Zhang, Alois Knoll
This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice.
no code implementations • 21 Jun 2023 • Aral Hekimoglu, Philipp Friedrich, Walter Zimmer, Michael Schmidt, Alvaro Marcos-Ramiro, Alois C. Knoll
In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation.
no code implementations • 15 Jun 2023 • Walter Zimmer, Christian Creß, Huu Tung Nguyen, Alois C. Knoll
Our dataset consists of 4. 8k images and point clouds with more than 57. 4k manually labeled 3D boxes.
no code implementations • 29 Apr 2023 • Walter Zimmer, Joseph Birkner, Marcel Brucker, Huu Tung Nguyen, Stefan Petrovski, Bohan Wang, Alois C. Knoll
We evaluate our results on the A9 infrastructure dataset and achieve 68. 48 mAP on the test set.
no code implementations • 11 Jul 2022 • Walter Zimmer, Jialong Wu, Xingcheng Zhou, Alois C. Knoll
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs.
no code implementations • 13 Apr 2022 • Christian Creß, Walter Zimmer, Leah Strand, Venkatnarayanan Lakshminarasimhan, Maximilian Fortkord, Siyi Dai, Alois Knoll
As part of the first set of data, which we describe in this paper, we provide camera and LiDAR frames from two overhead gantry bridges on the A9 autobahn with the corresponding objects labeled with 3D bounding boxes.
no code implementations • 31 Mar 2022 • Walter Zimmer, Emec Ercelik, Xingcheng Zhou, Xavier Jair Diaz Ortiz, Alois Knoll
The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.
no code implementations • 31 Mar 2022 • Walter Zimmer, Marcus Grabler, Alois Knoll
This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs.
2 code implementations • 1 May 2019 • Walter Zimmer, Akshay Rangesh, Mohan Trivedi
In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT).