EGAC3D: enhancing depth adaptive convolution and depth estimation for monocular 3D object pose detection.

ABSTRACT: Many alternative approaches for 3D object detection using a singular camera have been studied instead of leveraging high-precision 3D LiDAR sensors incurring a prohibitive cost. Recently, we proposed a novel approach for 3D object detection by employing a ground plane model that utilizes geometric constraints named GAC3D to improve the results of the deep-based detector. GAC3D adopts an adaptive depth convolution to replace the traditional 2D convolution to deal with the divergent context of the image's feature, leading to a significant improvement in both training convergence and testing accuracy on the KITTI 3D object detection benchmark. This article presents an alternative architecture named eGAC3D that adopts a revised depth adaptive convolution with variant guidance to improve detect

SUBMITTER: Ngo DT 

PROVIDER: S-EPMC9680869 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature