The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 531–538, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-531-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 531–538, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-531-2022
 
30 May 2022
30 May 2022

AUTOMATED GENERATION OF HIGH-QUALITY 3D POINT CLOUDS OF ANTLERS USING LOW-COST RANGE CAMERAS

S. Cheng1, D. D. Lichti1, and J. Matyas2 S. Cheng et al.
  • 1Dept. of Geomatics Engineering, University of Calgary, Calgary, AB, Canada
  • 2Dept. of Comparative Biology & Experimental Medicine, University of Calgary, Calgary, AB, Canada

Keywords: Range Cameras, Point Cloud, Antler, Registration, K-D Tree, NDT

Abstract. Three-dimensional imaging demonstrates advantages over traditional methods and has already proven feasible for measuring antler growth. However, antlers' velvet-covered surface and irregular structure pose challenges in efficiently obtaining high-quality antler data. Animal data capture using optical imaging devices and point cloud segmentation still require tedious manual work. To obtain 3D data of irregular biological targets like antlers, this paper proposes an automated workflow of high-quality 3D antler point cloud generation using low-cost range cameras. An imaging system of range cameras and one RGB camera is developed for automatic camera triggering and data collection without motion artifacts. The imaging system enables motion detection to ensure data collection occurs without any appreciable animal movement. The antler data are extracted automatically based on a fast k-d tree neighbor search to remove the irrelevant data. Antler point clouds from different cameras captured with various poses are aligned using target-based registration and the normal distribution transformation (NDT). The two-step registration demonstrates precisions of the overall RMSE of 4.8mm for the target-based method and Euclidean fitness score of 10.5mm for the NDT. Complete antler point clouds are generated with a higher density than that of individual frames and improved quality with outliers removed.