Despite significant progress in image-based 3D scene flow estimation, the
performance of such approaches has not yet reached the fidelity required by
many applications. Simultaneously, these applications are often not restricted
to image-based estimation: laser scanners provide a popular alternative to
traditional cameras, for example in the context of self-driving cars, as they
directly yield a 3D point cloud. In this paper, we propose to estimate 3D
motion from such unstructured point clouds using a deep neural network. In a
single forward pass, our model jointly predicts 3D scene flow as well as the 3D
bounding box and rigid body motion of objects in the scene. While the prospect
of estimating 3D scene flow from unstructured point clouds is promising, it is
also a challenging task. We show that the traditional global representation of
rigid body motion prohibits inference by CNNs, and propose a translation
equivariant representation to circumvent this problem. For training our deep
network, a large dataset is required. Because of this, we augment real scans
from KITTI with virtual objects, realistically modeling occlusions and
simulating sensor noise. A thorough comparison with classic and learning-based
techniques highlights the robustness of the proposed approach.

该论文提出了一种使用深度神经网络从非结构化点云中估计三维运动的方法，同时也能预测场景中物体的三维边界框和刚体运动。作者还采用虚拟物体增强真实扫描数据来训练网络，并与传统技术和基于学习的技术进行了全面比较。

PointFlowNet: 从点云学习刚体运动估计的表示

PointFlowNet: Learning Representations for Rigid Motion Estimation from  Point Clouds

We introduce SE3-Nets, which are deep neural networks designed to model and
learn rigid body motion from raw point cloud data. Based only on sequences of
depth images along with action vectors and point wise data associations,
SE3-Nets learn to segment effected object parts and predict their motion
resulting from the applied force. Rather than learning point wise flow vectors,
SE3-Nets predict SE3 transformations for different parts of the scene. Using
simulated depth data of a table top scene and a robot manipulator, we show that
the structure underlying SE3-Nets enables them to generate a far more
consistent prediction of object motion than traditional flow based networks.
Additional experiments with a depth camera observing a Baxter robot pushing
objects on a table show that SE3-Nets also work well on real data.

SE3- Nets 是深度神经网络，旨在通过原始点云数据来建模和学习刚体运动。它学习分割影响物体部分并预测由应用力导致的运动的 SE3 变换，相较于传统的基于流的神经网络，它能够产生更加一致的运动预测结果。