Abstract
Most existing 3D object detection that is researched on point clouds uses random down-sampling algorithms, which preserve the primary shape and feature information of the point clouds and extract robust features of sampling point clouds to decrease the consumption of computing resources and improve processing inference efficiency. On the other hand, this method of sampling may disregard significant areas within the point clouds, thereby diminishing the precision of detecting comparatively minute entities such as pedestrians and cyclists. In this research, an instance perception-based object detection method is presented that adaptively chooses additional foreground points in response to the properties of smaller objects in the initial point clouds. In addition, it offers end-to-end training and addresses the issue that the typical hand-designed multi-scale grouping receptive fields do not accurately reflect the size of the item. To effectively enhance the recall rate of foreground points, we present an instance-aware algorithm, which relies on farthest point sampling. Specifically, we employ dynamic gating networks to attain instance perception. This approach enables sample candidates to encompass a larger number of foreground objects. Then we use an aggregation layer to efficiently capture robust contextual features with rich spatial information. Through experiments performed on the KITTI dataset, we substantiate the effectiveness of our IAS when compared with current advanced 3D object detection methods. We have achieved a notable enhancement, especially in identifying diminutive objects.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.