  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.
