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了低层运动线索。现有最先进的学习方法通常速度较快，能在域内数据上取得令人瞩目的性能，但往往难以推广到分布外数据或处理稠密点云。本文聚焦于基于运行时优化的神经场景流管线。图(a)展示了其在激光雷达点云稠密化中的应用，但图(c)显示其主要缺点在于计算时间过长。我们发现，坐标网络中常见的网络架构加速策略（如图中绿色(b)所示）对场景流加速效果甚微，与图像重建（如图中粉色(b)所示）形成对比。鉴于主要计算负担源于Chamfer损失函数，我们提出基于距离变换的损失函数进行加速（如图中紫色(b)所示），相较于NSFP实现了高达30倍的加速比且估计性能持平。在8k点云测试中，该方法（如图中(c)所示）与领先学习方法同样高效，可实现实时处理。