PARAMETERS ACQUISITION FOR 3D TREE MODELING
USING THREE-LINE SENSOR
Hiroki MATSUDA, Masafumi NAKAGAWA, Ryosuke SHIBASAKI
Graduate School of Engineering, Department of Civil Engineering
and
Center for Spatial Information Science
The University of Tokyo
Cw-503, Block C, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan
(81)-3-5452-6417
(81)-3-5452-6414
Email: hmatsuda@iis.u-tokyo.ac.jp
ABSTRACT:
In Virtual Reality (VR), the development of various three-dimensional (3D) modelers enables
beautiful and faithful representation of urban landscape including buildings, streets and the other
urban facilities. Although model producers pay so much attention to the faithful representation
of man-made objects such as buildings, they usually determine tree model parameters such as
tree type, age, season without examining actual situations of the trees. Therefore, they are not
very realistic though they may look beautiful.
This paper aims to develop measures for producing a tree model more faithful to the actual
situations using high-resolution sensor data (e.g. Three Line Scanner (TLS) data, Vehicle-borne
Laser Mapping Scanner (VLMS) data. The procedure of the model production consists of the
following five phases: (1) tree extraction from sensor data, (2) tree’s volume estimation (height,
dimension, and diameter of trunk), (3) parameter determination, (4) initial model creation, and
(5) pruning.
(1) Some tree extraction methods have been already developed; therefore, we used these
methods. (2) Tree’s volume is estimated from TLS and VLMS data using a model-fitting
algorithm. (3) Tree’s type has to be provided manually, though, age is estimated by the diameter
of trunk. We’ve also automated this procedure. (4) Based on these parameters, an initial model is
produced using commercial 3D tree modeler. (5) Especially in urban area, trees are usually
pruned; therefore, the initial model may greatly differ from the reality. In order to remove this
difference, we’ve formulated a pruning procedure along with several rules to automate this
procedure. Finally, the reproduced tree models are quantitatively evaluated in terms of the
similarity against the real trees using their range images.
