Fast 3D object pose normalization for point cloud

Alexey Ruchay; Alexey Gladkov; Ramin Chelabiev

doi:10.1117/12.2593893

1 August 2021 Fast 3D object pose normalization for point cloud

Alexey Ruchay, Alexey Gladkov, Ramin Chelabiev

Proceedings Volume 11842, Applications of Digital Image Processing XLIV; 118421R (2021) https://doi.org/10.1117/12.2593893
Event: SPIE Optical Engineering + Applications, 2021, San Diego, California, United States

Conference Poster

Abstract

In this article, we proposed novel fast pose normalization algorithms for the point cloud. The first step deals with the detection of the ground plane of the scene in point cloud. Starting from downsampling point cloud by point cloud filtering and the normal vector of ground plane detected. The next step deals with the 3-D segmentation in point cloud, wherein we delete the ground plane. Then we used the algorithm of axis-aligned bounding box so that it sets the pose and dimensions of a box surrounding the given point cloud. Because we are computing an axis-aligned bounding box, the orientation of the box is just the identity orientation of the calculated unit normal vector to the plane. The algorithm of the axis-aligned bounding box is basically equivalent to taking the min/max of each coordinate. Moreover, we calculate the geometric center of the point cloud after pose normalization.

Citation Download Citation

Alexey Ruchay, Alexey Gladkov, and Ramin Chelabiev "Fast 3D object pose normalization for point cloud", Proc. SPIE 11842, Applications of Digital Image Processing XLIV, 118421R (1 August 2021); https://doi.org/10.1117/12.2593893

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