Oriented Point Sampling for Plane Detection in Unorganized Point Clouds

Plane detection in 3D point clouds is a crucial pre-processing step for applications such as point cloud segmentation, semantic mapping and SLAM. In contrast to many recent plane detection methods that are only applicable on organized point clouds, our work is targeted to unorganized point clouds that do not permit a 2D parametrization. We compare three methods for detecting planes in point clouds efficiently. One is a novel method proposed in this paper that generates plane hypotheses by sampling from a set of points with estimated normals. We named this method Oriented Point Sampling (OPS) to contrast with more conventional techniques that require the sampling of three unoriented points to generate plane hypotheses. We also implemented an efficient plane detection method based on local sampling of three unoriented points and compared it with OPS and the 3D-KHT algorithm, which is based on octrees, on the detection of planes on 10, 000 point clouds from the SUN RGB-D dataset.

Authors

• Bo Sun
• Philippos Mordohai

Keywords

• Three-dimensional displays
• Sun
• Surface treatment
• Clustering algorithms
• Octrees
• Estimation
• Image segmentation
• Point Cloud
• Detection In Point Clouds
• Unorganized Point Cloud
• 3D Point Cloud
• Simultaneous Localization And Mapping
• Point Cloud Segmentation
• Sampling Rate
• Classification Accuracy
• Horizontal Plane
• Average Accuracy
• Original Point
• Depth Images
• Segmentation Accuracy
• Depth Camera
• Vertical Plane
• Normal Approximation
• Hough Transform
• Inliers
• Signed Distance Function
• Surface Normals
• Fraction Of Points
• Plane Fitting
• Set Of Planes
• RGB-D Images
• Small Plane
• Multiple Orientations
• Merging Process
• Subset Of Points
• Cross-product