# Curriculum-Guided Adversarial Learning for Enhanced Robustness in 3D Object Detection

## TL;DR

This paper introduces a new framework to improve the robustness and accuracy of 3D object detection using LiDAR data through curriculum-guided adversarial learning.

## Contribution

The novel curriculum-guided adversarial learning framework (CGAL) enhances adversarial robustness in 3D object detection without adversarial training.

## Key findings

- The CGAL framework improves mean average precision (mAP) by 0.8–2.5 percentage points compared to conventional training methods.
- Models trained with the Adv-KITTI dataset show at least a 15 percentage point improvement in mAP.
- The proposed Pillar-RBFN detector demonstrates intrinsic adversarial robustness without adversarial training.

## Abstract

The pursuit of robust 3D object detection has emerged as a critical focus within the realm of computer vision. This paper presents a curriculum-guided adversarial learning (CGAL) framework, which significantly enhances the adversarial robustness and detection accuracy of the LiDAR-based 3D object detector PointPillars. By employing adversarial learning with prior curriculum expertise, this framework effectively resists adversarial perturbations generated by a novel attack method, P-FGSM, on 3D point clouds. By masterfully constructing a nonlinear enhancement block (NEB) based on the radial basis function network for PointPillars to adapt to the CGAL, a novel 3D object detector named Pillar-RBFN was developed; it exhibits intrinsic adversarial robustness without undergoing adversarial training. In order to tackle the class imbalance issue within the KITTI dataset, a data augmentation technique has been designed that singly samples the point cloud with additional ground truth objects frame by frame (SFGTS), resulting in the creation of an adversarial version of the original KITTI dataset named Adv-KITTI. Moreover, to further alleviate this issue, an adaptive variant of focal loss was formulated, effectively directing the model’s attention to challenging objects during the training process. Extensive experiments demonstrate that the proposed CGAL achieves an improvement of 0.8∼2.5 percentage points in mean average precision (mAP) compared to conventional training methods, and the models trained with Adv-KITTI have shown an enhancement of at least 15 percentage points in mAP, compellingly testifying to the effectiveness of our method.