Spatial reconstruction of biological trees from point clouds

Jayakumaran Ravi, Purdue University

Abstract

Trees are complex systems in nature whose topology and geometry are influenced by environmental factors. Tree geometry is extremely complicated and its capturing poses a challenging problem. Horticulturists require captured data of tree geometry to analyze regulation of resources. Traditionally, 3D digitizers and calipers are used to record position and orientation of every branch. While these are accurate, they are fundamentally time consuming. This thesis is an extension of our paper, Apple tree scanning and reconstruction using Kinect submitted to Acta Horticulturae. In our work we propose to reconstruct spatial data of Golden Delicious apple trees with user assistance. Our system requires a point cloud input to reconstruct the base tree. Our approach involves the use of Microsoft Kinect v2 sensor for scanning Golden Delicious apple trees. We extract a curve skeleton from the given point cloud and attach 2D shape proles along axes to generate a triangular mesh. Incomplete skeletal structures can be completed using skeleton editor tools provided in our GUI based application. Branch organs are reconstructed by sampling local points in the vicinity of curve skeleton obtained by the skeletonization algorithm. Direct sampling without establishing topological information can produce unrealistic visual results. We propose a sweeping reconstruction method which is capable of reconstructing branches starting from the root branch. Our method is capable of reconstructing branches which do not have enough sample points by using neighboring branches as reference. Results show that error in sweeping reconstruction is higher than directly sampled reconstruction. But this produces better visual results without any gaping holes in the 3D mesh model from a computer graphics perspective.

Degree

M.S.

Advisors

Benes, Purdue University.

Subject Area

Horticulture|Computer science

COinS