視覚言語モデルを用いた車両情報推定による自動運転向け3Dラベリングの改善
Improving 3D Labeling in Self-Driving by Inferring Vehicle Information using Vision Language Models
自動運転の3Dラベリング精度向上のため、視覚言語モデルで車両のメーカー・モデル・世代を推定し、正確な3Dバウンディングボックス寸法を出力する手法を提案。手動ラベリングの時間削減と品質向上を実証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Steven Chen, Shivesh Khaitan, Nemanja Djuric
分類: cs.CV, cs.RO
原文アブストラクト
We present an approach to improve 3D vehicle labeling in self-driving applications through zero-shot inference of vehicle information, leveraging Vehicle Make and Model Recognition (VMMR) methods. The proposed approach utilizes a Vision Language Model (VLM) to both infer a vehicle's make, model, and generation from image crops, and output accurate 3D bounding box dimensions to seed manual labeling. We evaluate the impact of iterative prompt engineering and the choice of different VLMs on both vehicle bounding box inference and make/model/generation recognition. When compared to strong baselines, the proposed approach not only shows high accuracy, but also excels in mitigating specific failure modes where VLMs provide better dimensions than initial lidar-aided human annotated labels (e.g., in cases of significant vehicle occlusion). Experiments on both public and proprietary data strongly suggest that our conclusions are generalizable across different labelers and datasets. The results demonstrate that integrating VLMs into the labeling process can reduce manual labeling time while increasing label quality.
