1 research outputs found
Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients
Conventional image motion based structure from motion methods first compute
optical flow, then solve for the 3D motion parameters based on the epipolar
constraint, and finally recover the 3D geometry of the scene. However, errors
in optical flow due to regularization can lead to large errors in 3D motion and
structure. This paper investigates whether performance and consistency can be
improved by avoiding optical flow estimation in the early stages of the
structure from motion pipeline, and it proposes a new direct method based on
image gradients (normal flow) only. The main idea lies in a reformulation of
the positive-depth constraint, which allows the use of well-known minimization
techniques to solve for 3D motion. The 3D motion estimate is then refined and
structure estimated adding a regularization based on depth. Experimental
comparisons on standard synthetic datasets and the real-world driving benchmark
dataset KITTI using three different optic flow algorithms show that the method
achieves better accuracy in all but one case. Furthermore, it outperforms
existing normal flow based 3D motion estimation techniques. Finally, the
recovered 3D geometry is shown to be also very accurate