论文标题
SF2SE3：聚类场景通过提案和选择流入SE（3） - 动作
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
论文作者
论文摘要
我们提出了SF2SE3，这是一种以分割形式估算场景动态为独立移动刚体对象及其SE（3） - 动作的新型方法。 SF2SE3在两个连续的立体声或RGB-D图像上运行。首先，通过应用现有的光流和深度估计算法获得嘈杂的场景流。然后迭代（1）样本像素集以计算SE（3） - 动作建议，（2）选择最佳的SE（3） - 动作建议，以最大值的覆盖面式。最后，通过基于与输入场景流量和空间接近的一致性将像素分配给所选的SE（3） - 动作来形成对象。主要的新颖性是对运动提案采样的更明智的策略，以及提案选择的最大覆盖范围。我们在多个数据集上进行了有关SF2SE3在场景流估计，对象分割和视觉上的应用的评估。 SF2SE3的表现与艺术的状态相同，以进行场景流量估计，并且更准确地进行分割和探光。
We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two consecutive stereo or RGB-D images. First, noisy scene flow is obtained by application of existing optical flow and depth estimation algorithms. SF2SE3 then iteratively (1) samples pixel sets to compute SE(3)-motion proposals, and (2) selects the best SE(3)-motion proposal with respect to a maximum coverage formulation. Finally, objects are formed by assigning pixels uniquely to the selected SE(3)-motions based on consistency with the input scene flow and spatial proximity. The main novelties are a more informed strategy for the sampling of motion proposals and a maximum coverage formulation for the proposal selection. We conduct evaluations on multiple datasets regarding application of SF2SE3 for scene flow estimation, object segmentation and visual odometry. SF2SE3 performs on par with the state of the art for scene flow estimation and is more accurate for segmentation and odometry.
