mlsanityData to 7 Oct 2026

EVOPS Benchmark: Evaluation of Plane Segmentation from RGBD and LiDAR Data

arXiv 2204.05799 ↗First posted 12 Apr 2022cs.CV cs.ROcited by 1

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Abstract (arXiv)

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/.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 50 references that are in the map. First posted on arXiv 12 Apr 2022.

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11 of 50 references.

References · 11

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2015ORB-SLAM: a Versatile and Accurate Monocular SLAM Systemcited by 983
  4. 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 801
  5. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  6. 2016Direct Sparse Odometrycited by 479
  7. 2021SuMa++: Efficient LiDAR-based Semantic SLAMcited by 100
  8. 2020LiDAR-based Panoptic Segmentation via Dynamic Shifting Networkcited by 84
  9. 2020BALM: Bundle Adjustment for Lidar Mappingcited by 27
  10. 2018Fast Cylinder and Plane Extraction from Depth Cameras for Visual Odometrycited by 6
  11. 2017Plane-extraction from depth-data using a Gaussian mixture regression modelcited by 1

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