4D Radar Ground Truth Augmentation with LiDAR-to-4D Radar Data Synthesis

• URL: http://arxiv.org/abs/2503.03637v1
• Date: Wed, 05 Mar 2025 16:16:46 GMT
• Title: 4D Radar Ground Truth Augmentation with LiDAR-to-4D Radar Data Synthesis
• Authors: Woo-Jin Jung, Dong-Hee Paek, Seung-Hyun Kong,
• Abstract summary: Ground truth augmentation (GT-Aug) is a common method for LiDAR-based object detection.<n>We propose 4D Radar Ground Truth Augmentation (4DR GT-Aug)<n>Our approach first augments LiDAR data and then converts it to 4D Radar data via a LiDAR-to-4D Radar data synthesis (L2RDaS) module.<n>In doing so, it produces 4D Radar data distributions that more closely resemble real-world measurements.
• Score: 6.605694475813286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ground truth augmentation (GT-Aug) is a common method for LiDAR-based object detection, as it enhances object density by leveraging ground truth bounding boxes (GT bboxes). However, directly applying GT-Aug to 4D Radar tensor data overlooks important measurements outside the GT bboxes-such as sidelobes-leading to synthetic distributions that deviate from real-world 4D Radar data. To address this limitation, we propose 4D Radar Ground Truth Augmentation (4DR GT-Aug). Our approach first augments LiDAR data and then converts it to 4D Radar data via a LiDAR-to-4D Radar data synthesis (L2RDaS) module, which explicitly accounts for measurements both inside and outside GT bboxes. In doing so, it produces 4D Radar data distributions that more closely resemble real-world measurements, thereby improving object detection accuracy. Experiments on the K-Radar dataset show that the proposed method achieves improved performance compared to conventional GT-Aug in object detection for 4D Radar. The implementation code is available at https://github.com/kaist-avelab/K-Radar.

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