论文标题
EVOPS基准：从RGBD和LIDAR数据中评估平面分割
EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data
论文作者
论文摘要
本文提供了来自3D数据的平面分割的EVOPS数据集，包括RGBD图像和LiDar Point Clouds。我们已经设计了两种注释方法（RGBD和LIDAR）在著名且广泛使用的数据集上进行SLAM评估，我们提供了一套完整的基准测试工具，包括点，飞机和细分指标。数据包括由不同选择的场景组成的不同选择场景的10K RGBD和7K LIDAR框架的总数。该实验报告了在我们的注释数据上进行RGBD平面分割的SOTA方法的质量。我们还为LIDAR点云中的平面分割提供了可学习的基线。所有标记的数据和基准工具均已在https://evops.netlify.app/上公开提供。
This paper provides the EVOPS dataset for plane segmentation from 3D data, both from RGBD images and LiDAR point clouds. We have designed two annotation methodologies (RGBD and LiDAR) running on well-known and widely-used datasets for SLAM evaluation and we have provided a complete set of benchmarking tools including point, planes and segmentation metrics. The data includes a total number of 10k RGBD and 7K LiDAR frames over different selected scenes which consist of high quality segmented planes. The experiments report quality of SOTA methods for RGBD plane segmentation on our annotated data. We also have provided learnable baseline for plane segmentation in LiDAR point clouds. All labeled data and benchmark tools used have been made publicly available at https://evops.netlify.app/.
