Outdoor 3D object detection has played an essential role in the environment perception of autonomous driving. In complicated traffic situations, precise object recognition provides indispensable information for prediction and planning in the dynamic system, improving self-driving safety and reliability. However, with the vehicle's veering, the constant rotation of the surrounding scenario makes a challenge for the perception systems. Yet most existing methods have not focused on alleviating the detection accuracy impairment brought by the vehicle's rotation, especially in outdoor 3D detection. In this paper, we propose DuEqNet, which first introduces the concept of equivariance into 3D object detection network by leveraging a hierarchical embedded framework. The dual-equivariance of our model can extract the equivariant features at both local and global levels, respectively. For the local feature, we utilize the graph-based strategy to guarantee the equivariance of the feature in point cloud pillars. In terms of the global feature, the group equivariant convolution layers are adopted to aggregate the local feature to achieve the global equivariance. In the experiment part, we evaluate our approach with different baselines in 3D object detection tasks and obtain State-Of-The-Art performance. According to the results, our model presents higher accuracy on orientation and better prediction efficiency. Moreover, our dual-equivariance strategy exhibits the satisfied plug-and-play ability on various popular object detection frameworks to improve their performance.

翻译：户外三维目标检测在自动驾驶环境感知中扮演着关键角色。在复杂交通场景下，精确的目标识别为动态系统的预测与规划提供了不可或缺的信息，从而提升自动驾驶的安全性和可靠性。然而，随着车辆转向，周遭场景的持续旋转给感知系统带来挑战。现有方法大多未着力缓解车辆旋转导致的检测精度损失问题，尤其在户外三维检测领域。本文提出DuEqNet，首次通过层级嵌入式框架将等变概念引入三维目标检测网络。该模型的双等变性可分别提取局部与全局层面的等变特征：针对局部特征，采用基于图的策略确保点云柱中特征的等变性；在全局特征方面，利用群等变卷积层聚合局部特征以实现全局等变性。实验部分，我们在三维目标检测任务中与不同基线方法进行对比评估，取得了最优性能。结果表明，本模型在朝向精度和预测效率上均表现更优。此外，该双等变策略在多种主流目标检测框架中展现出良好的即插即用能力，可显著提升其检测性能。