Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown
remarkable adaptability in the context of large out-of-distribution autonomous
driving. Despite their success, the underlying reasons for their astonishing
generalization capabilities remain unclear. Our research addresses this gap by
examining the generalization capabilities of NSFP through the lens of uniform
stability, revealing that its performance is inversely proportional to the
number of input point clouds. This finding sheds light on NSFP's effectiveness
in handling large-scale point cloud scene flow estimation tasks. Motivated by
such theoretical insights, we further explore the improvement of scene flow
estimation by leveraging historical point clouds across multiple frames, which
inherently increases the number of point clouds. Consequently, we propose a
simple and effective method for multi-frame point cloud scene flow estimation,
along with a theoretical evaluation of its generalization abilities. Our
analysis confirms that the proposed method maintains a limited generalization
error, suggesting that adding multiple frames to the scene flow optimization
process does not detract from its generalizability. Extensive experimental
results on large-scale autonomous driving Waymo Open and Argoverse lidar
datasets demonstrate that the proposed method achieves state-of-the-art
performance.

通过对大规模点云场景流估计任务的统一稳定性进行理论分析，揭示了 Neural Scene Flow Prior（NSFP）在处理大规模点云场景流估计任务中的有效性。我们进一步探索了利用历史多帧点云改进场景流估计的方法，并在理论和实验结果上验证了其广义性。大规模自动驾驶 Waymo Open 和 Argoverse 激光雷达数据集上的大量实验结果表明，所提出的方法取得了最先进的性能。

自监督多帧神经场景流

Self-Supervised Multi-Frame Neural Scene Flow

Neural Scene Flow Prior (NSFP) is of significant interest to the vision
community due to its inherent robustness to out-of-distribution (OOD) effects
and its ability to deal with dense lidar points. The approach utilizes a
coordinate neural network to estimate scene flow at runtime, without any
training. However, it is up to 100 times slower than current state-of-the-art
learning methods. In other applications such as image, video, and radiance
function reconstruction innovations in speeding up the runtime performance of
coordinate networks have centered upon architectural changes. In this paper, we
demonstrate that scene flow is different -- with the dominant computational
bottleneck stemming from the loss function itself (i.e., Chamfer distance).
Further, we rediscover the distance transform (DT) as an efficient,
correspondence-free loss function that dramatically speeds up the runtime
optimization. Our fast neural scene flow (FNSF) approach reports for the first
time real-time performance comparable to learning methods, without any training
or OOD bias on two of the largest open autonomous driving (AV) lidar datasets
Waymo Open and Argoverse.

本文提出了一种名为 “快速神经场景流”（FNSF）的方法，通过使用距离变换（DT）作为损失函数，使运行时间大大缩短，使其无需训练和偏见，从而实现了与当前最先进的学习方法相当的实时性能并成功在两个最大的自主驾驶（AV）激光雷达数据集 Waymo Open 和 Argoverse 上运行。

快速神经场景流

Fast Neural Scene Flow

Before the deep learning revolution, many perception algorithms were based on
runtime optimization in conjunction with a strong prior/regularization penalty.
A prime example of this in computer vision is optical and scene flow.
Supervised learning has largely displaced the need for explicit regularization.
Instead, they rely on large amounts of labeled data to capture prior
statistics, which are not always readily available for many problems. Although
optimization is employed to learn the neural network, the weights of this
network are frozen at runtime. As a result, these learning solutions are
domain-specific and do not generalize well to other statistically different
scenarios. This paper revisits the scene flow problem that relies predominantly
on runtime optimization and strong regularization. A central innovation here is
the inclusion of a neural scene flow prior, which uses the architecture of
neural networks as a new type of implicit regularizer. Unlike learning-based
scene flow methods, optimization occurs at runtime, and our approach needs no
offline datasets -- making it ideal for deployment in new environments such as
autonomous driving. We show that an architecture based exclusively on
multilayer perceptrons (MLPs) can be used as a scene flow prior. Our method
attains competitive -- if not better -- results on scene flow benchmarks. Also,
our neural prior's implicit and continuous scene flow representation allows us
to estimate dense long-term correspondences across a sequence of point clouds.
The dense motion information is represented by scene flow fields where points
can be propagated through time by integrating motion vectors. We demonstrate
such a capability by accumulating a sequence of lidar point clouds.

本文重点研究基于运行时优化和强正则化的场景流问题，创新性地引入了神经场景流先验，其使用神经网络体系结构作为一种新类型的隐式正则化器，该方法可以用于无需脱机数据集而部署在新环境的、包括车载应用的自主驾驶型设备的场景。通过对激光雷达点云序列的积累来展示该神经先验的连续场景流表示方法，并达到了竞争性甚至更好的场景流基准结果。