EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data
This provides a new benchmark for evaluating plane segmentation methods in computer vision, addressing a domain-specific need for researchers working with RGBD and LiDAR data.
The paper introduces the EVOPS dataset for plane segmentation from 3D data, including 10k RGBD and 7k LiDAR frames with high-quality annotations, and benchmarks state-of-the-art methods on this data.
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/.