Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation
| Autor: | Steven L. Waslander, Ali Harakeh, Jungwook Lee, Sean Walsh |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Computer Science - Artificial Intelligence Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre Machine Learning (cs.LG) Minimum bounding box Statistics - Machine Learning 0502 economics and business 0202 electrical engineering electronic engineering information engineering Segmentation 050210 logistics & transportation Ground truth Artificial neural network business.industry 05 social sciences Object (computer science) Object detection Artificial Intelligence (cs.AI) 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ITSC |
| DOI: | 10.48550/arxiv.1807.06072 |
| Popis: | Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. This paper introduces a novel ground truth generation method that combines human supervision with pre-trained neural networks to generate per-instance 3D point cloud segmentation, 3D bounding boxes, and class annotations. The annotators provide object anchor clicks which behave as a seed to generate instance segmentation results in 3D. The points belonging to each instance are then used to regress object centroids, bounding box dimensions, and object orientation. Our proposed annotation scheme requires 30x lower human annotation time. We use the KITTI 3D object detection dataset [1] to evaluate the efficiency and the quality of our annotation scheme. We also test the the proposed scheme on previously unseen data from the Autonomoose self-driving vehicle to demonstrate generalization capabilities of the network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|