3D Surface Reconstruction of Plant Seeds by Volume Carving

Abstract

We describe a method for 3D reconstruction of plant seed surfaces, focusing on small seeds with diameters as small as 200 µm. The method considers robotized systems allowing single seed handling in order to rotate a single seed in front of a camera. Even though such systems feature high position repeatability, at sub-millimeter object scales, camera pose variations have to be compensated. We do this by robustly estimating the tool center point from each acquired image. 3D reconstruction can then be performed by a simple shape-from-silhouette approach. In experiments we investigate runtimes, the achieved accuracy, and show as a proof of principle that the proposed method is well sufficient for 3D seed phenotyping purposes.

Fast High Resolution Volume Carving for 3D Plant Shoot Reconstruction

“…Measuring plant geometry from single view-point 2D images often suffers from insufficient information, especially when plant organs occlude each other (self-occlusion). In order to achieve more detailed information and recover the plants 3D geometric structure volume carving is a well established method to generate 3D point clouds of plant shoots (Koenderink et al, 2009 ; Golbach et al, 2015 ; Klodt and Cremers, 2015 ), seeds (Roussel et al, 2015 , 2016 ; Jahnke et al, 2016 ), and roots (Clark et al, 2011 ; Zheng et al, 2011 ; Topp et al, 2013 ). Volume carving can be applied in high-throughput scenarios (Golbach et al, 2015 ): For the reconstruction of relatively simple plant structures like tomato seedlings image reconstruction takes ~25–60 ms, based on a well though out camera geometry using 10 cameras and a suitably low voxel resolution 240 × 240 × 300 voxels at 0.25 mm voxel width.…”

3D Surface Reconstruction of Plant Seeds by Volume Carving: Performance and Accuracies

“…This paper is an extension of our conference publication (Roussel et al, 2015 ), thus theory (Section 2 and 3), and some experiments from Section 4 are mainly repeated from there. We extend the theory by an accuracy check and iterative camera position correction procedure, and the experiments by a numerical and experimental investigation of achievable accuracy vs. number of images in Sections 4.2 and 4.3.…”