论文标题
plg-in：在单眼深度估计中，瓦斯汀距离的可插入几何一致性损失
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation
论文作者
论文摘要
我们提出了一个新的目标，以惩罚几何不一致，以改善单眼相机图像的深度和构成估计性能。我们的目标是使用两个点云之间的Wasserstein距离设计的，这是根据具有不同相机姿势的图像估计的。 Wasserstein距离可以在两个点云之间施加柔软和对称的耦合，这适当地保持了几何限制，并导致可区分的目标。通过将我们的目标添加到其他最先进方法的目标中，我们可以有效地惩罚几何不一致，并获得高度准确的深度和姿势估计。使用KITTI数据集评估我们提出的方法。
We propose a novel objective for penalizing geometric inconsistencies to improve the depth and pose estimation performance of monocular camera images. Our objective is designed using the Wasserstein distance between two point clouds, estimated from images with different camera poses. The Wasserstein distance can impose a soft and symmetric coupling between two point clouds, which suitably maintains geometric constraints and results in a differentiable objective. By adding our objective to the those of other state-of-the-art methods, we can effectively penalize geometric inconsistencies and obtain highly accurate depth and pose estimations. Our proposed method is evaluated using the KITTI dataset.
