  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/.

Computer Vision and Pattern Recognition;Robotics