用知识蒸馏简化点云3D检测的多尺度特征，提升效率与精度。
Transferable Class Statistics and Multi-scale Feature Approximation for 3D Object Detection
- 单邻域内通过知识蒸馏近似多尺度特征，减少计算开销。
- 引入可迁移的类别统计特征，弥补单一邻域信息不足。
- 适合资源受限场景，对轻量级3D检测任务有实用价值。
本文研究点云中多尺度特征近似与可迁移特征在3D目标检测中的应用。多尺度特征对点云目标检测至关重要，但通常需多次邻域搜索和尺度感知层，增加计算负担，不利于轻量化模型构建或资源受限场景。为此，本文基于知识蒸馏，在单个邻域内近似点云多尺度特征。为弥补单邻域导致的结构多样性损失，设计了可迁移特征嵌入机制，利用计算成本低的类别感知统计量作为可迁移特征。此外，引入中心加权交并比（central weighted IoU）进行定位优化，缓解因中心偏移带来的位置偏差。大量实验在公开数据集上验证了所提方法的有效性，显著降低计算开销。
原文摘要 · Abstract (English)
This paper investigates multi-scale feature approximation and transferable features for object detection from point clouds. Multi-scale features are critical for object detection from point clouds. However, multi-scale feature learning usually involves multiple neighborhood searches and scale-aware layers, which can hinder efforts to achieve lightweight models and may not be conducive to research constrained by limited computational resources. This paper approximates point-based multi-scale features from a single neighborhood based on knowledge distillation. To compensate for the loss of constructive diversity in a single neighborhood, this paper designs a transferable feature embedding mechanism. Specifically, class-aware statistics are employed as transferable features given the small computational cost. In addition, this paper introduces the central weighted intersection over union for localization to alleviate the misalignment brought by the center offset in optimization. Note that the method presented in this paper saves computational costs. Extensive experiments on public datasets demonstrate the effectiveness of the proposed method.
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