SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences
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DOI：
10.1109/wacv.2018.00121
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发表时间：
2017-10
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影响因子：
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通讯作者：
René Schuster;Oliver Wasenmüller;G. Kuschk;C. Bailer;D. Stricker
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作者：
René Schuster;Oliver Wasenmüller;G. Kuschk;C. Bailer;D. Stricker
虽然大多数场景流方法使用变分优化或强刚性运动假设，但我们首次证明场景流也可以通过稀疏匹配的密集插值来估计。为此，我们在没有任何事先正则化的情况下检测到的两个立体图像对之间找到稀疏匹配，并通过使用边缘信息执行密集插值来保留几何和运动边界。我们执行了几次变分能量最小化迭代来完善我们的结果，这些结果在 KITTI 基准上进行了全面评估，并与 MPI Sintel 上的最新技术进行了比较。对于汽车环境中的应用，我们进一步表明，可选的自我运动模型有助于提高性能，并顺利地融入我们的方法中，将场景分割为静态和动态部分。
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.