论文标题
变形和对应关系意识到点云的无监督的合成到现场场景流量估计
Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds
论文作者
论文摘要
点云场景流量估计对于自动驾驶中的动态场景导航至关重要。由于很难获得场景流标签，因此当前方法将其模型训练在合成数据上，并将其传输到真实场景。但是，现有的合成数据集与真实场景之间的巨大差异导致模型转移差。我们为解决这个问题做出了两个主要贡献。首先，我们为GTA-V发动机开发了一个点云收集器和场景流注释器，以自动获取不同的现实训练样本而无需人工干预。因此，我们开发了一个大规模的合成场景流数据集GTA-SF。其次，我们提出了一个基于平均教师的域适应框架，该框架利用目标域的自我生成的伪标记。它还明确地结合了形状变形正则化和表面对应关系，以解决域转移中的扭曲和未对准。通过广泛的实验，我们表明我们的GTA-SF数据集与最广泛使用的FT3D数据集相比，对三个真实数据集（即Waymo，Lyft和Kitti）的模型概括（即Waymo，Lyft和Kitti）的一致增强。此外，我们的框架在六个源目标数据集对上实现了出色的适应性性能，使平均域间隙截断了60％。数据和代码可在https://github.com/leolyj/dca-srsfe上找到
Point cloud scene flow estimation is of practical importance for dynamic scene navigation in autonomous driving. Since scene flow labels are hard to obtain, current methods train their models on synthetic data and transfer them to real scenes. However, large disparities between existing synthetic datasets and real scenes lead to poor model transfer. We make two major contributions to address that. First, we develop a point cloud collector and scene flow annotator for GTA-V engine to automatically obtain diverse realistic training samples without human intervention. With that, we develop a large-scale synthetic scene flow dataset GTA-SF. Second, we propose a mean-teacher-based domain adaptation framework that leverages self-generated pseudo-labels of the target domain. It also explicitly incorporates shape deformation regularization and surface correspondence refinement to address distortions and misalignments in domain transfer. Through extensive experiments, we show that our GTA-SF dataset leads to a consistent boost in model generalization to three real datasets (i.e., Waymo, Lyft and KITTI) as compared to the most widely used FT3D dataset. Moreover, our framework achieves superior adaptation performance on six source-target dataset pairs, remarkably closing the average domain gap by 60%. Data and codes are available at https://github.com/leolyj/DCA-SRSFE
