用摄像头验证激光雷达检测结果，大幅减少误报。
VaLID: Verification as Late Integration of Detections for LiDAR-Camera Fusion
- 先用激光雷达生成检测框，再用摄像头数据验证其真伪。
- 在KITTI上平均降低63.9%误报，3DAP优于单独模型。
- 适用于通用摄像头模型，无需特定训练，适合实际部署。
车辆目标检测受益于激光雷达与摄像头的融合，其中激光雷达在多数场景下表现更优。现有融合方法常引入复杂性或依赖特定数据集。本文提出一种模型自适应的晚融合方法VaLID，通过验证每个预测边界框是否合理来提升精度。该方法利用摄像头检测结果（来自专用、通用或开放词汇模型）验证性能更强但可能过于乐观的激光雷达检测。VaLID采用轻量级神经验证网络，以高召回率偏差进行训练，有效减少激光雷达检测的误报，同时保留真实检测。在KITTI数据集上，使用多种激光雷达与摄像头检测器组合测试，平均误报降低63.9%，3D平均精度（3DAP）超越单一模型。该方法具有模型自适应性，在未针对该数据集专门训练的通用摄像头模型下仍达到领先水平。
原文摘要 · Abstract (English)
Vehicle object detection benefits from both LiDAR and camera data, with LiDAR offering superior performance in many scenarios. Fusion of these modalities further enhances accuracy, but existing methods often introduce complexity or dataset-specific dependencies. In our study, we propose a model-adaptive late-fusion method, VaLID, which validates whether each predicted bounding box is acceptable or not. Our method verifies the higher-performing, yet overly optimistic LiDAR model detections using camera detections that are obtained from either specially trained, general, or open-vocabulary models. VaLID uses a lightweight neural verification network trained with a high recall bias to reduce the false predictions made by the LiDAR detector, while still preserving the true ones. Evaluating with multiple combinations of LiDAR and camera detectors on the KITTI dataset, we reduce false positives by an average of 63.9%, thus outperforming the individual detectors on 3D average precision (3DAP). Our approach is model-adaptive and demonstrates state-of-the-art competitive performance even when using generic camera detectors that were not trained specifically for this dataset.
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