1,072 research outputs found

    Fast Multi-frame Stereo Scene Flow with Motion Segmentation

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    We propose a new multi-frame method for efficiently computing scene flow (dense depth and optical flow) and camera ego-motion for a dynamic scene observed from a moving stereo camera rig. Our technique also segments out moving objects from the rigid scene. In our method, we first estimate the disparity map and the 6-DOF camera motion using stereo matching and visual odometry. We then identify regions inconsistent with the estimated camera motion and compute per-pixel optical flow only at these regions. This flow proposal is fused with the camera motion-based flow proposal using fusion moves to obtain the final optical flow and motion segmentation. This unified framework benefits all four tasks - stereo, optical flow, visual odometry and motion segmentation leading to overall higher accuracy and efficiency. Our method is currently ranked third on the KITTI 2015 scene flow benchmark. Furthermore, our CPU implementation runs in 2-3 seconds per frame which is 1-3 orders of magnitude faster than the top six methods. We also report a thorough evaluation on challenging Sintel sequences with fast camera and object motion, where our method consistently outperforms OSF [Menze and Geiger, 2015], which is currently ranked second on the KITTI benchmark.Comment: 15 pages. To appear at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Our results were submitted to KITTI 2015 Stereo Scene Flow Benchmark in November 201

    Implementation and Qualitative/ Quantitative Comparison and Evaluation of Range Flow and Scene Flow

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    This thesis investigates the use of 3D scene flow and 3D range flow as a means of computing 3D observer (sensor or camera) motion. We implemented and evaluated the scene flow algorithm presented in Stereoscopic Scene Flow Computation for 3D Motion Understanding by Wedel et al. 2010. We modified (performed pyramidal image processing with warping) and re-implemented the range flow algorithms pre­ sented in Quantitative Regularized Range Flow by Spies et al. 2000. Both algorithms are 2-frame algorithms using a pyramid. The results for these scene and range flow algorithms were quantitatively and qualitatively compared on synthetic and real car driving stereo sequences

    SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences

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    While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201
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