论文标题
从单眼伪LIDAR检测自我监督的3D对象检测
Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR
论文作者
论文摘要
试图通过融合立体声相机图像和LiDAR传感器数据或使用LIDAR进行预训练，而仅用于测试的单眼图像来检测3D对象，但是由于精确度较低而仅尝试使用单眼图像序列的尝试较少。另外，当仅使用单眼图像进行深度预测时，只能预测尺度不一致的深度，这就是研究人员不愿单独使用单眼图像的原因。因此，我们提出了一种通过仅使用单眼图像序列来预测绝对深度和检测3D对象的方法，通过启用检测网络和深度预测网络的端到端学习。结果，所提出的方法超过了Kitti 3D数据集中性能的其他现有方法。即使在训练期间一起使用单眼图像和3D激光雷达以提高性能，与使用相同输入的其他方法相比，我们的展览也是最佳性能。此外，端到端学习不仅可以改善深度预测性能，而且还可以实现绝对深度预测，因为我们的网络利用了这样一个事实，即3D对象（例如汽车）的大小由大约大小确定。
There have been attempts to detect 3D objects by fusion of stereo camera images and LiDAR sensor data or using LiDAR for pre-training and only monocular images for testing, but there have been less attempts to use only monocular image sequences due to low accuracy. In addition, when depth prediction using only monocular images, only scale-inconsistent depth can be predicted, which is the reason why researchers are reluctant to use monocular images alone. Therefore, we propose a method for predicting absolute depth and detecting 3D objects using only monocular image sequences by enabling end-to-end learning of detection networks and depth prediction networks. As a result, the proposed method surpasses other existing methods in performance on the KITTI 3D dataset. Even when monocular image and 3D LiDAR are used together during training in an attempt to improve performance, ours exhibit is the best performance compared to other methods using the same input. In addition, end-to-end learning not only improves depth prediction performance, but also enables absolute depth prediction, because our network utilizes the fact that the size of a 3D object such as a car is determined by the approximate size.
