Abstract
LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse distant points. In this work, we study the varying-sparsity distribution of LiDAR points and present SphereFormer to directly aggregate information from dense close points to the sparse distant ones. We design radial window self-attention that partitions the space into multiple non-overlapping narrow and long windows. It overcomes the disconnection issue and enlarges the receptive field smoothly and dramatically, which significantly boosts the performance of sparse distant points. Moreover, to fit the narrow and long windows, we propose exponential splitting to yield fine-grained position encoding and dynamic feature selection to increase model representation ability. Notably, our method ranks 1st on both nuScenes and SemanticKITTI semantic segmentation benchmarks with 81.9% and 74.8% mIoU, respectively. Also, we achieve the 3rd place on nuScenes object detection benchmark with 72.8% NDS and 68.5% mAP. Code is available at this https URL.
Abstract (translated)
利用激光雷达点云识别3D点云的方法可以造福多种应用程序。如果没有特别考虑激光雷达点云分布,大多数当前方法都面临信息断开和接收域有限的问题,特别是对于稀疏遥远的点。在这项工作中,我们研究了激光雷达点云的 varying-sparss分布,并提出了Sphere Former直接聚合从密集接近点到稀疏遥远的信息。我们设计了径向窗口自注意力,将空间划分为多个非重叠的窄长窗口。它克服了信息断开的问题,并且极大地扩展了接收域,这极大地提高了稀疏遥远的点的性能。此外,为了适应窄长窗口,我们提出了指数分割,生成精细的位置编码和动态特征选择,以提高模型表示能力。值得注意的是,我们的方法在nuScenes和SemanticKITTI语义分割基准测试中分别获得了81.9%和74.8%的mIoU,同时,在nuScenes物体检测基准测试中获得了第3名,72.8%的NDS和68.5%的mAP。代码在此httpsURL上可用。
URL
https://arxiv.org/abs/2303.12766
