Abstract
Abstract. In this work, we report a novel way of generating ground truth dataset for analyzing point cloud from different sensors and the validation of algorithms. Instead of directly labeling large amount of 3D points requiring time consuming manual work, a multi-resolution 3D voxel grid for the testing site is generated. Then, with the help of a set of basic labeled points from the reference dataset, we can generate a 3D labeled space of the entire testing site with different resolutions. Specifically, an octree-based voxel structure is applied to voxelize the annotated reference point cloud, by which all the points are organized by 3D grids of multi-resolutions. When automatically annotating the new testing point clouds, a voting based approach is adopted to the labeled points within multiple resolution voxels, in order to assign a semantic label to the 3D space represented by the voxel. Lastly, robust line- and plane-based fast registration methods are developed for aligning point clouds obtained via various sensors. Benefiting from the labeled 3D spatial information, we can easily create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation.
Highlights
In the past decade, the automatic 3D scene analysis using point clouds has attracted increasing attentions in research fields of photogrammetry (Vosselman and Maas, 2010), remote sensing (Lefsky et al, 1999), computer vision (Buch et al, 2011), and robotics (Rusu et al, 2009)
Benefiting from the labeled 3D spatial information, we can create new annotated 3D point clouds of different sensors of the same scene directly by considering the corresponding labels of 3D space the points located, which would be convenient for the validation and evaluation of algorithms related to point cloud interpretation and semantic segmentation
We proposed a novel strategy of automatically generating ground truth dataset for analyzing point cloud from different sensors and validation of algorithms
Summary
The automatic 3D scene analysis using point clouds has attracted increasing attentions in research fields of photogrammetry (Vosselman and Maas, 2010), remote sensing (Lefsky et al, 1999), computer vision (Buch et al, 2011), and robotics (Rusu et al, 2009). For any proposed algorithms and methods, satisfying experiments and convincing evaluations are always non-trivial and crucial steps to validate the feasibility and performance of the proposed method. To conduct such experiments and evaluations, the benchmark dataset or the ground truth are normally required.
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