ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002 
PERCEPTUAL ORGANIZATION OF 3D SURFACE POINTS 
Impyeong Lee, Toni Schenk 
Dept. of Civil and Environmental Engineering and Geodetic Science 
The Ohio State University, Columbus, OH 43017, USA 
lee.1517 G osu.edu, schenk.2 9 osu.edu 
Commission III, WG III/3 
KEY WORDS: Photogrammetry, Vision Sciences, Organization, Segmentation, Surface, LIDAR, Grouping, Points 
ABSTRACT: 
Perceptual organization is proposed as a promising intermediate process toward object recognition and reconstruction 
from 3D surface points, which can be derived from aerial stereo-images, LIDAR data or InSAR data. Here, percep- 
tual organization is to group sensory primitives originating from the same object and has been emphasized as a robust 
intermediate-level grouping process toward object recognition in human and computer vision. Despite intensive research 
on 2D data, perceptual organization of 3D entities is still in its infancy, however. Therefore, the purpose of this research is 
to develop a robust approach for constructing perceptual organization particularly with irregularly distributed 3D surface 
points. The scope of perceptual organization presented in this paper is limited to signal, primitive and structural levels. 
At the signal level, we organize raw 3D points into spatially coherent patches. Then, at the primitive level, we merge the 
patches into co-parametric surfaces. Finally, at the structural level, we group the surfaces into perceptually meaningful 
surface clusters. We establish a novel approach and implement the approach as an autonomous system. The system is 
evaluated with real LIDAR data by inspecting the quality of organized output. The evaluation substantiates a promising 
performance of the system. The organized output serves as a valuable input to higher order perceptual processes, including 
the generation and validation of hypotheses in object recognition tasks. 
1. INTRODUCTION 
A far-reaching goal of digital photogrammetry is to re- 
construct the world with an abstract description generated 
from various sensory inputs as autonomously as possible. 
This inversion problem is ill-posed and the solutions are 
usually based on introducing assumptions about the 3D ob- 
ject space and by applying suitable constraints. It has long 
been recognized that surfaces play an important role in 
the quest of reconstructing scenes from sensory data such 
as images. Surfaces are predominantly represented (mea- 
sured) by irregularly distributed 3D points. Such a point 
cloud can be derived from aerial imagery (stereopsis), LI- 
DAR data or InSAR data. 
Perceptual organization deals with grouping sensory in- 
puts that originate from the same object by finding struc- 
tural regularity from or imposing structural organization on 
the inputs. It has been recognized as a crucial component 
that makes human perception powerful and versatile. In 
computer vision, perceptual organization is often used as a 
robust intermediate-vision process toward object recogni- 
tion. 
Sarkar and Boyer (1993) propose a classificatory structure 
of perceptual organization based on the dimension over 
which an organization is sought and the abstraction level 
of features to be grouped. The structure has two axes: one 
axis denotes 2D, 3D, 2D plus time and 3D plus time; and 
the other axis represents signal, primitive, structural and 
assembly levels. For example, segmentation of surfaces 
from a 3D point cloud is classified into 3D signal level per- 
ceptual organization. In addition, further grouping of seg- 
mented surfaces is categorized into 3D primitive or struc- 
tural level perceptual organization. 
Previous work in perceptual organization concentrated on 
2D organization, dealing with all the abstraction levels and 
emphasizing the structural level. In 3D organization, most 
previous studies address only the signal level, particularly 
focusing on range image segmentation (Koster and Spann, 
2000; Jiang et al., 2000; Liu and Wang, 1999; Hoover et 
al., 1996). The need of perceptual organization in vari- 
ous levels of 3D will increase because 3D sensors have 
become cheaper and more readily available. Boyer and 
Sarkar (1999) conclude that perceptual organization in 3D 
is one of the most important research directions in this area. 
Therefore, the purpose of the research reported in this pa- 
per is to develop a robust approach for constructing percep- 
tual organization, particularly with irregularly distributed 
3D surface points. In the interest of brevity we focus on 
the most salient features of the proposed approach. The 
interested reader may find missing details in Lee (2002). 
2. PROPOSED APPROACH 
A conceptual framework for computing perceptual orga- 
nization from 3D surface points is described by Lee and 
Schenk (2001). Under this framework, we have devel- 
oped a novel approach that is based on a bottom-up (or 
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