Real-time and high-performance 3D object detection is of critical importance for autonomous driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel-based convolutions, which are both computationally inefficient for onboard deployment. In contrast, pillar-based methods use solely 2D convolutions, which consume less computation resources, but they lag far behind their voxel-based counterparts in detection accuracy. In this paper, by examining the primary performance gap between pillar- and voxel-based detectors, we develop a real-time and high-performance pillar-based detector, dubbed PillarNet.The proposed PillarNet consists of a powerful encoder network for effective pillar feature learning, a neck network for spatial-semantic feature fusion and the commonly used detect head. Using only 2D convolutions, PillarNet is flexible to an optional pillar size and compatible with classical 2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits from our designed orientation-decoupled IoU regression loss along with the IoU-aware prediction branch. Extensive experimental results on the large-scale nuScenes Dataset and Waymo Open Dataset demonstrate that the proposed PillarNet performs well over state-of-the-art 3D detectors in terms of effectiveness and efficiency. Code is available at \url{https://github.com/agent-sgs/PillarNet}.
翻译:对自动驾驶而言,最新高性能的3D天体探测器主要依靠点基或3D voxel 的变异,这些变异在计算上效率低下。相比之下,基于支柱的方法只使用2D变异,它们消耗的计算资源较少,但在检测准确性方面却远远落后于基于 voxel 的对等方。在本文件中,我们通过审查基于支柱和 voxel 的探测器之间的主要性能差距,开发了一个基于支柱的实时和高性能的支柱探测器,称为支柱网。拟议的支柱网包括一个强大的编码网络,用于有效的支柱特征学习、空间-地震特征融合的颈网络和常用的探测头部。仅使用2D变异,支柱网具有灵活性,与传统的 2D WNCN 脊柱(如VGGNet和ResNet)相容。此外,与IOU-aware 3Dgr 预测处一起,我们设计的定向-DOB-Dawreabl 的IODS-S-Crevelopalalal-stal lavemental develop lades lavelop laves lax-dalstal-dalst lavelopmentals pass lavelmentals lavelmentals pass