论文标题
对LiDar深度图和应用的无监督信心
Unsupervised confidence for LiDAR depth maps and applications
论文作者
论文摘要
在许多领域（例如机器人技术和自动驾驶）中，深度感知至关重要。因此，在许多应用中，诸如激光雷达之类的深度传感器迅速传播。这些传感器生成的3D点云通常必须与RGB摄像头结合，以便以语义理解框架场景。通常，前者投影在摄像机图像平面上，导致深度稀疏的地图。不幸的是，此过程以及影响所有深度传感器的内在问题，在最终输出中产生噪声和粗离标机。在本文中，我们提出了一个有效的无监督框架，旨在通过学习估算LiDar稀疏深度图的信心，从而允许过滤离群值来明确解决此问题。 Kitti数据集的实验结果强调，我们的框架为此目的出色。此外，我们演示了这项成就如何改善广泛的任务。
Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications. The 3D point clouds generated by these sensors must often be coupled with an RGB camera to understand the framed scene semantically. Usually, the former is projected over the camera image plane, leading to a sparse depth map. Unfortunately, this process, coupled with the intrinsic issues affecting all the depth sensors, yields noise and gross outliers in the final output. Purposely, in this paper, we propose an effective unsupervised framework aimed at explicitly addressing this issue by learning to estimate the confidence of the LiDAR sparse depth map and thus allowing for filtering out the outliers. Experimental results on the KITTI dataset highlight that our framework excels for this purpose. Moreover, we demonstrate how this achievement can improve a wide range of tasks.
