Scene flow is an important problem as it provides low-level motion cues for many downstream tasks. State-of-the-art learning methods are usually fast and can achieve impressive performance on in-domain data, but usually fail to generalize to out-of-the-distribution (OOD) data or handle dense point clouds. In this paper, we focus on a runtime optimization-based neural scene flow pipeline. In (a) one can see its application in the densification of lidar. However, in (c) one sees that the major drawback is the extensive computation time. We identify that the common speedup strategy in network architectures for coordinate networks has little effect on scene flow acceleration [see green (b)] unlike image reconstruction [see pink (b)]. With the dominant computational burden stemming instead from the Chamfer loss function, we propose to use a distance transform-based loss function to accelerate [see purple (b)], which achieves up to 30x speedup and on-par estimation performance compared to NSFP [see (c)]. When tested on 8k points, it is as efficient [see (c)] as leading learning methods, achieving real-time performance.
翻译:场景流是一个重要问题,因为它为许多下游任务提供低水平的运动线索。现有的学习方法通常快速,并且可以在领域内数据上实现令人印象深刻的性能,但通常无法推广到越界数据或处理密集点云。在本文中,我们专注于一种基于运行时优化的神经场景流管道。在(a)中,可以看到它在激光雷达密集化中的应用。然而,在(c)中,主要的缺点是广泛的计算时间。我们发现,网络架构中用于坐标网络的常见加速策略对场景流加速几乎没有影响[见绿色(b)],而图像重建[见粉色(b)]具有显著影响。由于主要的计算负担来自Chamfer损失函数,因此我们提出使用基于距离变换的损失函数加速[见紫色(b)],其达到了高达30倍的加速和与NSFP[见(c)]相当的估计性能。当在8k个点上测试时,它的效率[见(c)]与领先的学习方法一样,实现了实时性能。