Point Contrastive learning for LiDAR-based 3D object detection in autonomous driving

Current progress in 3D Perception tasks for autonomous driving relies upon neural network architectures that their training requires a growing demand for annotated data. However, semantic annotation of 3D scenes is a very expensive and labor-intensive task. In this paper, we present an approach for...

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Bibliographic Details
Main Authors: Karypidis, Efstathios, Zamanakos, Georgios, Tsochatzidis, Lazaros, Pratikakis, Ioannis
Format: Conference Proceeding
Language:English
Subjects:
3D Object Detection
Detectors
Digital signal processing
LiDAR
Object detection
Point cloud compression
Point Clouds
Self-Supervised Learning
Semantics
Three-dimensional displays
Training
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Summary:Current progress in 3D Perception tasks for autonomous driving relies upon neural network architectures that their training requires a growing demand for annotated data. However, semantic annotation of 3D scenes is a very expensive and labor-intensive task. In this paper, we present an approach for self-supervised, data-efficient learning in the context of point contrastive learning, using two distinct pre-training techniques towards improving performance in LiDAR-based 3D object detection in autonomous driving. Our experimental work relies upon standard benchmarking datasets, namely KITTI and Waymo. Under a comprehensive evaluation framework it is shown that, in the absence of large annotated data, the proposed approach could achieve improved performance.
ISSN:2165-3577
DOI:10.1109/DSP58604.2023.10167978