1 research outputs found
PV-SSD: A Projection and Voxel-based Double Branch Single-Stage 3D Object Detector
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