论文标题
3D深：基于高程模式的3维深学习
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
论文作者
论文摘要
道路检测和细分是用于安全自动驾驶的计算机视觉中的至关重要任务。考虑到这一点，沿本文描述了一种新的网络体系结构（3D深）及其基于CNN的语义细分的端到端培训方法。该方法依赖于差异过滤和激光雷达图像，以获取三维信息和图像特征通过完全卷积网络体系结构提取。使用19种不同的培训课程和Kitti Road数据集对CityScapes数据集进行了培训和验证。使用验证图像获得了19个CityScapes培训课程的72.32％的联合（MIOU）平均值（MIOU）。另一方面，使用测试图像，模型在Kittidataset上获得了97.85％无效的F1误差值，而96.02％的误差值为96.02％。
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training methodology for CNN-based semantic segmentation are described along this paper for. The method relies on disparity filtered and LiDAR projected images for three-dimensional information and image feature extraction through fully convolutional networks architectures. The developed models were trained and validated over Cityscapes dataset using just fine annotation examples with 19 different training classes, and over KITTI road dataset. 72.32% mean intersection over union(mIoU) has been obtained for the 19 Cityscapes training classes using the validation images. On the other hand, over KITTIdataset the model has achieved an F1 error value of 97.85% invalidation and 96.02% using the test images.
