通过多尺度注意力提升点云3D检测，更准识别小物体和语义关联目标。
LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network
- 引入多尺度注意力机制融合局部几何与全局上下文信息
- 在ScanNetv2上实现mAP@25提升近1%，mAP@50提升4.78%
- 适用于需要高精度小物体检测的场景理解任务
点云数据中的3D目标检测因输入稀疏且缺乏全局结构而面临挑战。本文提出将多尺度注意力（MSA）机制融入3DETR架构，以更好捕捉局部几何与全局上下文。方法引入上采样操作生成高分辨率特征图，使网络能更准确检测小型及语义相关目标。在ScanNetv2数据集上的实验表明，3DETR + MSA模型相比基线在mAP@25上提升近1%，在mAP@50上提升4.78%。尽管在3DETR-m变体上改进有限，分析揭示轻量级模型需适配上采样策略。结果表明，层次化特征提取与注意力机制结合可有效提升3D场景理解能力。
原文摘要 · Abstract (English)
3D object detection in point cloud data remains a challenging task due to the sparsity and lack of global structure inherent in the input. In this work, we propose a novel Multi-Scale Attention (MSA) mechanism integrated into the 3DETR architecture to better capture both local geometry and global context. Our method introduces an upsampling operation that generates high-resolution feature maps, enabling the network to better detect smaller and semantically related objects. Experiments conducted on the ScanNetv2 dataset demonstrate that our 3DETR + MSA model improves detection performance, achieving a gain of almost 1% in mAP@25 and 4.78% in mAP@50 over the baseline. While applying MSA to the 3DETR-m variant shows limited improvement, our analysis reveals the importance of adapting the upsampling strategy for lightweight models. These results highlight the effectiveness of combining hierarchical feature extraction with attention mechanisms in enhancing 3D scene understanding.
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