3D scene flow presents the 3D motion of each point in the 3D space,
which forms the fundamental 3D motion perception for autonomous driving
and server robots. Although the RGBD camera or LiDAR capture discrete 3D
points in space, the objects and motions usually are continuous in the
macro world. That is, the objects keep themselves consistent as they
flow from the current frame to the next frame. Based on this insight,
the Generative Adversarial Networks (GAN) is utilized to self-learn 3D
scene flow with no need for ground truth. The fake point cloud of the
second frame is synthesized from the predicted scene flow and the point
cloud of the first frame. The adversarial training of the generator and
discriminator is realized through synthesizing indistinguishable fake
point cloud and discriminating the real point cloud and the synthesized
fake point cloud. The experiments on KITTI scene flow dataset show that
our method realizes promising results without ground truth. Just like a
human observing a real-world scene, the proposed approach is capable of
determining the consistency of the scene at different moments in spite
of the exact flow value of each point is unknown in advance.
Corresponding author(s)
Email: [email protected]
Basis constrained 3D scene flow on a dynamic proxy
