LIDAR-based 3D object detection and classification is crucial for autonomous driving. However, inference in real-time from extremely sparse 3D data poses a formidable challenge. To address this issue, a common approach is to project point clouds onto a bird's-eye or perspective view, effectively converting them into an image-like data format. However, this excessive compression of point cloud data often leads to the loss of information. This paper proposes a 3D object detector based on voxel and projection double branch feature extraction (PV-SSD) to address the problem of information loss. We add voxel features input containing rich local semantic information, which is fully fused with the projected features in the feature extraction stage to reduce the local information loss caused by projection. A good performance is achieved compared to the previous work. In addition, this paper makes the following contributions: 1) a voxel feature extraction method with variable receptive fields is proposed; 2) a feature point sampling method by weight sampling is used to filter out the feature points that are more conducive to the detection task; 3) the MSSFA module is proposed based on the SSFA module. To verify the effectiveness of our method, we designed comparison experiments.

翻译：基于激光雷达的3D目标检测与分类对于自动驾驶至关重要。然而，从极度稀疏的3D数据中进行实时推理构成了严峻挑战。为解决此问题，一种常见方法是将点云投影到鸟瞰图或透视图上，有效将其转换为类似图像的数据格式。但这种对点云数据的过度压缩常导致信息损失。本文提出一种基于体素与投影双分支特征提取的3D目标检测器（PV-SSD），以解决信息丢失问题。我们引入包含丰富局部语义信息的体素特征输入，并在特征提取阶段与投影特征充分融合，从而减少投影导致的局部信息损失。与先前工作相比，该方法取得了良好的性能。此外，本文做出以下贡献：1）提出一种具有可变感受野的体素特征提取方法；2）采用基于权重采样的特征点采样方法，以筛选出更有利于检测任务的特征点；3）基于SSFA模块提出了MSSFA模块。为验证方法的有效性，我们设计了对比实验。