  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 are publicly available on
https://github.com/ctu-vras/let-it-flow.
