FLAVA：LiDAR点群アノテーションを効率化する発見・位置特定・調整・検証パイプライン
FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds
LiDAR点群のアノテーション作業を「発見・位置特定・調整・検証」の4段階に分け、段階ごとにUIを最適化しアノテーション結果の伝播機構を導入することで、人手の介入を大幅に削減し作業の高速化と品質向上を実現した。
著者: Tai Wang, Conghui He, Zhe Wang, Jianping Shi, Dahua Lin
分類: cs.RO, cs.CV, cs.HC
原文アブストラクト
Recent years have witnessed the rapid progress of perception algorithms on top of LiDAR, a widely adopted sensor for autonomous driving systems. These LiDAR-based solutions are typically data hungry, requiring a large amount of data to be labeled for training and evaluation. However, annotating this kind of data is very challenging due to the sparsity and irregularity of point clouds and more complex interaction involved in this procedure. To tackle this problem, we propose FLAVA, a systematic approach to minimizing human interaction in the annotation process. Specifically, we divide the annotation pipeline into four parts: find, localize, adjust and verify. In addition, we carefully design the UI for different stages of the annotation procedure, thus keeping the annotators to focus on the aspects that are most important to each stage. Furthermore, our system also greatly reduces the amount of interaction by introducing a light-weight yet effective mechanism to propagate the annotation results. Experimental results show that our method can remarkably accelerate the procedure and improve the annotation quality.
