论文标题
3D场景流量估计伪LIDAR：在估计点运动上弥合差距
3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion
论文作者
论文摘要
3D场景流动表征了当前时间的点如何流到3D欧几里得空间中的下一次，该空间具有自主推断场景中所有对象的非刚性运动的能力。从图像估算场景流的先前方法具有局限性，该方法通过分别估计光流和差异来划分3D场景流的整体性质。学习3D场景从点云流动也面临着综合数据和真实数据之间的差距以及LiDar Point云的稀疏性之间的困难。在本文中，生成的密集深度图用于获得显式的3D坐标，从而直接从2D图像中获得了3D场景流的直接学习。通过将2D像素的密度性质引入3D空间，可以提高预测场景流的稳定性。通过统计方法删除生成的3D点云中的离群值，以削弱嘈杂点对3D场景流量估计任务的影响。提出了差异一致性损失，以实现3D场景流的更有效的无监督学习。比较了实际图像上3D场景流的自我监督学习方法与在综合数据集中学习的多种方法和在激光雷达点云上学习的方法。显示多个场景流量指标的比较证明了引入伪lidar点云到场景流量估计的有效性和优势。
3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation.
