Abstract: In 3D object detection from point clouds, the inherent sparsity of LiDAR data poses pronounced challenges for small objects. Few effective points lead to weak structural cues and blurry boundaries; limited contextual awareness hinders spatial reasoning and semantic completion, causing localization bias; and the difficulty of precise spatial localization, weak channel expressiveness, and background dominance jointly constrain accuracy. To mitigate the impact of the above issues on detection accuracy, we propose a dynamic-aware 3D detector that integrates dynamic feature extraction with feature-enhancement mapping, targeting the two critical stages of small-object detection—feature extraction and candidate generation. Specifically, we introduce a dynamic point-feature prediction network that adaptively predicts and supplements sampling points to strengthen structural perception of small objects; we then build a feature-enhancement mapping network that deeply fuses the original features with those produced by the dynamic module to yield context-rich 2D feature maps, thereby compensating for contextual deficiency and improving localization; finally, we design a point-cloud feature-enhancement module to sharpen focus on key small-object regions along both channel and spatial dimensions. Experiments on the nuScenes dataset demonstrate that our approach surpasses mainstream detectors: relative to the CenterPoint baseline, mean Average Precision (mAP) increases from 56.1% to 59.4%, and the nuScenes Detection Score (NDS) rises from 64.4% to 67.4%.

摘要： 在点云三维目标检测任务中，点云数据的稀疏性客观上对小目标检测构成显著挑战。具体表现为：小目标自身有效点数稀少导致结构信息缺失与边界模糊；上下文感知能力不足阻碍模型有效利用周围环境信息进行空间推理与语义补全，进而引发定位偏差；以及其固有的空间定位困难、通道表达弱和特征易被背景淹没等问题，共同制约了检测性能的提升。为缓解上述问题对检测精度造成的影响，本文提出一种融合动态特征提取与特征增强映射的动态感知三维检测算法。该模型聚焦特征提取与候选框生成两大关键阶段对小目标检测进行优化。具体而言：首先，引入动态点特征预测网络，通过自适应预测补充采样点以强化对小目标的结构感知能力；其次，构建特征增强映射网络，对原始特征及动态预测网络生成的特征进行深度融合，输出富含上下文信息的二维特征图，有效弥补上下文缺失并提升小目标的定位精度；最后，设计点云特征增强网络，在通道与空间双维度提升网络对小目标关键区域的聚焦能力。基于nuScenes数据集的实验结果表明，所提算法性能优于当前主流目标检测算法。与基准模型CenterPoint相比，平均精度（mAP）由56.1%提升至59.4%；标准化检测分数（NDS）由64.4%提升至67.4%。