A Continuous Optimization Approach for Efficient and Accurate Scene Flow
This work addresses the need for efficient and accurate scene flow estimation in computer vision, particularly for applications like autonomous driving, though it is incremental as it builds on existing planar segment representations.
The authors tackled the dense 3D scene flow problem from stereo imagery by proposing a continuous optimization method that avoids complex discrete-continuous formulations, achieving third place on the KITTI Scene Flow benchmark while being 3 to 30 times faster than top competitors.
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.