GA-EIRFS：一种用于长尾LiDAR 3D目标检测的几何增强重复因子采样方法
GA-EIRFS: A Geometry-Augmented Repeat-Factor Sampling Method for Long-Tailed LiDAR 3D Object Detection
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中文总结 AI 辅助
提出GA-EIRFS，一种通过几何分数调制频率重复因子的采样方法，在不改变检测器的情况下提升长尾LiDAR 3D检测性能，在nuScenes上显著提高mAP和NDS。
中文摘要 AI 辅助
长尾3D目标检测通常被视为类别频率问题，但LiDAR监督质量取决于目标的可观测性：相似的频率可能隐藏不同的几何证据。我们提出了几何增强指数加权实例感知重复因子采样（GA-EIRFS），这是一种与检测器无关的方法，它通过结合点数量、表面法线熵和表面覆盖率的固定几何分数来调制基于频率的重复因子。GA-EIRFS仅改变帧采样概率，保持检测器和推理不变。在nuScenes上，它在使用CenterPoint和PointPillars的四个收敛实验中，在两个种子下提高了平均精度（mAP）和nuScenes检测分数（NDS）；对于种子666下的CenterPoint，mAP从0.552提升至0.563，自行车AP从0.306提升至0.359。每类增益与类别采样权重增加相关（Spearman rho=0.70，p=0.025），但仅与几何分数不相关（rho=0.32，p=0.37），因此几何放大了频率驱动的需求。KITTI结果在不同种子间有所差异，最稀有类别差异最大。代码：此https URL。
英文摘要
Long-tailed 3D object detection is treated as a class-frequency problem, but LiDAR supervision quality depends on object observability: similar frequencies can hide different geometric evidence. We introduce Geometry-Augmented Exponentially Weighted Instance-Aware Repeat Factor Sampling (GA-EIRFS), a detector-agnostic method that modulates a frequency-based repeat factor with a fixed geometry score combining point count, surface-normal entropy, and surface coverage. GA-EIRFS changes only frame-sampling probabilities, leaving the detector and inference unchanged. On nuScenes it improves mean average precision (mAP) and the nuScenes detection score (NDS) in four converged experiments with CenterPoint and PointPillars over two seeds; for CenterPoint at seed 666, mAP rises from 0.552 to 0.563 and bicycle AP from 0.306 to 0.359. Per-class gains correlate with the class sampling-weight increase (Spearman rho=0.70, p=0.025) but not with geometry score alone (rho=0.32, p=0.37), so geometry amplifies frequency-driven need. KITTI results vary across seeds, most for the rarest class. Code: https://github.com/Multimodal-Sensing-Lab/GA-EIRFS.
发表机构
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心）
- University of Jyväskylä（于韦斯屈莱大学）
机构由 AI 辅助整理，请以论文原文为准。
