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Found 14 papers, 4 papers with code
TUMTraf V2X Cooperative Perception Dataset
We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task.
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
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.
ActiveAnno3D - An Active Learning Framework for Multi-Modal 3D Object Detection
We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training.
TUMTraf Event: Calibration and Fusion Resulting in a Dataset for Roadside Event-Based and RGB Cameras
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.
A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques.
Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior
This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice.
Multi-Task Consistency for Active Learning
In single-task vision-based settings, inconsistency-based active learning has proven to be effective in selecting informative samples for annotation.
A9 Intersection Dataset: All You Need for Urban 3D Camera-LiDAR Roadside Perception
Our dataset consists of 4. 8k images and point clouds with more than 57. 4k manually labeled 3D boxes.
InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors
We evaluate our results on the A9 infrastructure dataset and achieve 68. 48 mAP on the test set.
Real-Time And Robust 3D Object Detection with Roadside LiDARs
This work aims to address the challenges in autonomous driving by focusing on the 3D perception of the environment using roadside LiDARs.
A9-Dataset: Multi-Sensor Infrastructure-Based Dataset for Mobility Research
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.
Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation
This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs.
A Survey of Robust 3D Object Detection Methods in Point Clouds
The purpose of this work is to review the state-of-the-art LiDAR-based 3D object detection methods, datasets, and challenges.
3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams
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).
