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.
Runtime and accuracy comparison of detection results on the KITTI test set. We compare our model with voxel-based methods on the car class for three difficulty levels. Voxel-based methods using pseudo image representations (Voxel-PI) are shown as circles, and 3D voxel-based methods (Voxel-3D) are plotted as triangles. Our method gives a better compromise in terms of accuracy and runtime for all cases.
SE: SECOND ; PP: PointPillars; TA: TANet; AS: Associate-3D; 3D: 3DIoULoss; SA: SA-SSD; HS: HotSpotNet.
An overview of our framework. The HVPR network inputs point clouds and generates two types of hybrid 3D features via a two-stream encoder: Voxel-point and voxel-memory representations. The former representations are obtained by aggregating point-based features for individual voxel-based ones. For the later ones, we also perform the aggregation but with memory items, instead of using point-based features. That is, we augment the point-based features using a memory module, and exploit voxel-memory representations, i.e., hybrid 3D features, at test time for fast inference. The backbone network with AMFM inputs the voxel-memory representations to extract multiple scale-aware features, and the detection head predicts 3D bounding boxes and object classes.
J. Noh, S.Lee, B. Ham|
HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021
[Paper on arXiv] [Code will be released soon]
This research was partly supported by R&D program for Advanced Integrated-intelligence for Identification (AIID) through the National Research Foundation of KOREA (NRF) funded by Ministry of Science and ICT (NRF-2018M3E3A1057289), and Institute for Information and Communications Technology Promotion (IITP) funded by the Korean Government (MSIP) under Grant 2016-0-00197.