  We propose a continuous optimization method for solving dense 3D scene flow
problems from stereo imagery. As in recent work, we represent the dynamic 3D
scene as a collection of rigidly moving planar segments. The scene flow problem
then becomes the joint estimation of pixel-to-segment assignment, 3D position,
normal vector and rigid motion parameters for each segment, leading to a
complex and expensive discrete-continuous optimization problem. In contrast, we
propose a purely continuous formulation which can be solved more efficiently.
Using a fine superpixel segmentation that is fixed a-priori, we propose a
factor graph formulation that decomposes the problem into photometric,
geometric, and smoothing constraints. We initialize the solution with a novel,
high-quality initialization method, then independently refine the geometry and
motion of the scene, and finally perform a global non-linear refinement using
Levenberg-Marquardt. We evaluate our method in the challenging KITTI Scene Flow
benchmark, ranking in third position, while being 3 to 30 times faster than the
top competitors.
