Safe autonomous agents and mobile robots need fast real time 3D perception, especially for vulnerable road users (VRUs) such as pedestrians. We introduce a new bird's eye view (BEV) encoding, which maps the full 3D LiDAR point cloud into a light-weight 2D BEV tensor with three height bands. We explicitly reformulate 3D detection as a 2D detection problem and then reconstruct 3D boxes from the BEV outputs. A single network detects cars, pedestrians, and cyclists in one pass. The backbone uses area attention at deep stages, a hierarchical bidirectional neck over P1 to P4 fuses context and detail, and the head predicts oriented boxes with distribution focal learning for side offsets and a rotated IoU loss. Training applies a small vertical re bin and a mild reflectance jitter in channel space to resist memorization. We use an interquartile range (IQR) filter to remove noisy and outlier LiDAR points during 3D reconstruction. On KITTI dataset, TriBand-BEV attains 58.7/52.6/47.2 pedestrian BEV AP(%) for easy, moderate, and hard at 49 FPS on a single consumer GPU, surpassing Complex-YOLO, with gains of +12.6%, +7.5%, and +3.1%. Qualitative scenes show stable detection under occlusion. The pipeline is compact and ready for real time robotic deployment. Our source code is publicly available on GitHub.

翻译：安全自动驾驶智能体与移动机器人需要快速实时的三维感知能力，尤其对行人等弱势道路使用者的检测至关重要。本文提出一种新型鸟瞰图编码方法，将完整三维激光雷达点云映射为轻量级二维BEV张量，并采用三个高度波段实现特征提取。我们将三维检测显式重构为二维检测问题，进而从BEV输出重建三维包围框。单一网络即可一次性完成汽车、行人、骑行者检测。主干网络在深层采用区域注意力机制，P1至P4层级层次化双向颈部网络实现上下文与细节融合，检测头采用分布焦点学习预测侧向偏移的朝向框及旋转交并比损失。训练过程中，在通道空间应用小幅度垂直重新分箱与温和的反射率抖动以抵抗过拟合。三维重建阶段使用四分位距滤波去除噪声与离群点激光雷达点。在KITTI数据集上，TriBand-BEV在单消费级GPU上以49帧/秒速度运行，行人BEV平均精度分别达到简易/中等/困难难度下的58.7%/52.6%/47.2%，超越Complex-YOLO方法，增益分别达+12.6%、+7.5%及+3.1%。定性场景表明该方法在遮挡条件下仍具备稳定检测能力。整套流水线紧凑且可直接部署于实时机器人系统。本方法源代码已在GitHub上开源。