论文标题
4Denoisenet：不利的天气从相邻点云
4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds
论文作者
论文摘要
可靠的点云数据对于机器人技术和自主驾驶应用程序中的感知任务\ textit {e.g。}至关重要。不利的天气会导致特定类型的噪声检测和范围（LIDAR）传感器数据，从而大大降低了点云的质量。为了解决这个问题，这封信提出了一种新颖的点云不利天气，使深度学习算法（4Denoisenet）。我们的算法利用了时间维度，与文献中的深度学习不利的天气变质方法不同。与以前的工作相比，它的交叉点比联合度量的交点更好10 \％，并且在计算上更有效。这些结果是在我们的新型Snowkitti数据集上实现的，该数据集具有超过40000个不良天气注释点云。此外，对加拿大不利驾驶条件数据集的强烈定性结果表明，良好的概括性对域移动和不同传感器内在的概括。
Reliable point cloud data is essential for perception tasks \textit{e.g.} in robotics and autonomous driving applications. Adverse weather causes a specific type of noise to light detection and ranging (LiDAR) sensor data, which degrades the quality of the point clouds significantly. To address this issue, this letter presents a novel point cloud adverse weather denoising deep learning algorithm (4DenoiseNet). Our algorithm takes advantage of the time dimension unlike deep learning adverse weather denoising methods in the literature. It performs about 10\% better in terms of intersection over union metric compared to the previous work and is more computationally efficient. These results are achieved on our novel SnowyKITTI dataset, which has over 40000 adverse weather annotated point clouds. Moreover, strong qualitative results on the Canadian Adverse Driving Conditions dataset indicate good generalizability to domain shifts and to different sensor intrinsics.
