We address the problem of 3D object detection, that is, estimating 3D object
bounding boxes from point clouds. 3D object detection methods exploit either
voxel-based or point-based features to represent 3D objects in a scene.
Voxel-based features are efficient to extract, while they fail to preserve
fine-grained 3D structures of objects. Point-based features, on the other hand,
represent the 3D structures more accurately, but extracting these features is
computationally expensive. We introduce in this paper a novel single-stage 3D
detection method having the merit of both voxel-based and point-based features.
To this end, we propose a new convolutional neural network (CNN) architecture,
dubbed HVPR, that integrates both features into a single 3D representation
effectively and efficiently. Specifically, we augment the point-based features
with a memory module to reduce the computational cost. We then aggregate the
features in the memory, semantically similar to each voxel-based one, to obtain
a hybrid 3D representation in a form of a pseudo image, allowing to localize 3D
objects in a single stage efficiently. We also propose an Attentive Multi-scale
Feature Module (AMFM) that extracts scale-aware features considering the sparse
and irregular patterns of point clouds. Experimental results on the KITTI
dataset demonstrate the effectiveness and efficiency of our approach, achieving
a better compromise in terms of speed and accuracy.

本文提出了一种新的单级三维检测方法 HVPR，它将点云的体素特征与点特征有效且高效地集成到一个伪图像的混合三维表示中，从而实现了三维对象的定位。同时，我们提出了一种 Attentive Multi-scale Feature Module（AMFM），用于考虑点云的稀疏和不规则分布，以提取具有尺度感知的特征，并在 KITTI 数据集上验证了我们方法的有效性和效率。