Accurate Representation Modeling and Interindividual Constraint Learning for Roadside Three-Dimensional Object Detection

Roadside three-dimensional (3-D) object detection is essential for enhancing blind-less perception performance in cooperative autonomous driving. Current research has preliminarily explored the representations from different perspectives and constraints inside individuals. However, the accuracy of existing representations is hardly guaranteed under the wide-range conditions on roadside applications, and the constraints involving multiple targets are less considered. To address these, an accurate representation modeling and interindividual constraint-learning method is proposed. In representation modeling, the deficiencies of existing distance representations are systematically analyzed, and the relative depth is developed with the consideration of numerical distribution and error tendency under wide-range conditions. Besides, the limitation of existing reference-pixel representation is addressed by introducing Affine–Gaussian heatmaps, which accurately selects reference pixels and depresses extra responses based on geometric discrepancies. In constraint learning, the interindividual constraints are effectively extracted in the proposed graph-based module, which introduces powerful graph attention operations and a special designed implicit gradient flow to induce constraints into image feature maps. Extensive experiments on DAIR-V2X-I and Rope3-D demonstrate that significant improvements are achieved compared with concurrent state-of-arts on both the trained scenarios and unseen scenarios.