用语义标签替代框标注，实现更高效的3D目标检测。
Seg2Box: 3D Object Detection by Point-Wise Semantics Supervision
- 通过时序一致性聚类生成精准伪框标签
- 在Waymo和nuScenes上mAP提升23.7%和10.3%
- 适合追求少标注、高效率的3D感知场景
基于激光雷达的3D目标检测与语义分割是3D场景理解的关键任务。传统方法分别使用边界框和语义掩码进行监督，但二者存在显著冗余。本文提出Seg2Box，仅用语义标签监督3D目标检测，以消除冗余。针对点云实例几何不完整与边界模糊导致的伪标签不准问题，我们设计多帧多尺度聚类（MFMS-C）模块，利用点云时空一致性生成准确的框级伪标签；并引入语义引导的迭代挖掘自训练（SGIM-ST）模块，逐步优化伪标签并挖掘无伪标签实例。在Waymo Open Dataset和nuScenes数据集上的实验表明，本方法的mAP分别比现有方法提升23.7%和10.3%，验证了其在标签高效性方面的巨大潜力与先进性。
原文摘要 · Abstract (English)
LiDAR-based 3D object detection and semantic segmentation are critical tasks in 3D scene understanding. Traditional detection and segmentation methods supervise their models through bounding box labels and semantic mask labels. However, these two independent labels inherently contain significant redundancy. This paper aims to eliminate the redundancy by supervising 3D object detection using only semantic labels. However, the challenge arises due to the incomplete geometry structure and boundary ambiguity of point-cloud instances, leading to inaccurate pseudo labels and poor detection results. To address these challenges, we propose a novel method, named Seg2Box. We first introduce a Multi-Frame Multi-Scale Clustering (MFMS-C) module, which leverages the spatio-temporal consistency of point clouds to generate accurate box-level pseudo-labels. Additionally, the Semantic?Guiding Iterative-Mining Self-Training (SGIM-ST) module is proposed to enhance the performance by progressively refining the pseudo-labels and mining the instances without generating pseudo-labels. Experiments on the Waymo Open Dataset and nuScenes Dataset show that our method significantly outperforms other competitive methods by 23.7\% and 10.3\% in mAP, respectively. The results demonstrate the great label-efficient potential and advancement of our method.
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