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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