We study the problem of self-supervised 3D scene flow estimation from real
large-scale raw point cloud sequences, which is crucial to various tasks like
trajectory prediction or instance segmentation. In the absence of ground truth
scene flow labels, contemporary approaches concentrate on deducing optimizing
flow across sequential pairs of point clouds by incorporating structure based
regularization on flow and object rigidity. The rigid objects are estimated by
a variety of 3D spatial clustering methods. While state-of-the-art methods
successfully capture overall scene motion using the Neural Prior structure,
they encounter challenges in discerning multi-object motions. We identified the
structural constraints and the use of large and strict rigid clusters as the
main pitfall of the current approaches and we propose a novel clustering
approach that allows for combination of overlapping soft clusters as well as
non-overlapping rigid clusters representation. Flow is then jointly estimated
with progressively growing non-overlapping rigid clusters together with fixed
size overlapping soft clusters. We evaluate our method on multiple datasets
with LiDAR point clouds, demonstrating the superior performance over the
self-supervised baselines reaching new state of the art results. Our method
especially excels in resolving flow in complicated dynamic scenes with multiple
independently moving objects close to each other which includes pedestrians,
cyclists and other vulnerable road users. Our codes will be publicly available.

我们研究了从真实大规模原始点云序列中自监督估计 3D 场景流的问题，该问题对于轨迹预测或实例分割等各种任务至关重要。我们提出了一种新颖的聚类方法，允许组合重叠的软聚类和非重叠的刚性聚类表示。我们的方法在多个具有 LiDAR 点云的数据集上进行了评估，证明了优于自监督基线的卓越性能，达到了最新的技术水平。特别是在解决含有行人、骑自行车者和其他易受伤害道路用户的复杂动态场景中，我们的方法表现出色。我们的代码将公开提供。

让其流动：3D 流动和物体聚类的同时优化

Let It Flow: Simultaneous Optimization of 3D Flow and Object Clustering

Learning without supervision how to predict 3D scene flows from point clouds
is central to many vision systems. We propose a novel learning framework for
this task which improves the necessary regularization. Relying on the
assumption that scene elements are mostly rigid, current smoothness losses are
built on the definition of ``rigid clusters" in the input point clouds. The
definition of these clusters is challenging and has a major impact on the
quality of predicted flows. We introduce two new consistency losses that
enlarge clusters while preventing them from spreading over distinct objects. In
particular, we enforce \emph{temporal} consistency with a forward-backward
cyclic loss and \emph{spatial} consistency by considering surface orientation
similarity in addition to spatial proximity. The proposed losses are
model-independent and can thus be used in a plug-and-play fashion to
significantly improve the performance of existing models, as demonstrated on
two top-performing ones. We also showcase the effectiveness and generalization
capability of our framework on four standard sensor-unique driving datasets,
achieving state-of-the-art performance in 3D scene flow estimation. Our codes
are available anonymously on https://github.com/vacany/sac-flow.

无监督学习如何从点云中预测 3D 场景流是许多视觉系统的核心，本文提出了一种新颖的学习框架来改进必要的正则化，并通过引入两种新的一致性损失来扩大聚类，并防止它们扩散到不同的对象上，提高了现有模型的性能。