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###
Building Volumetric Models from
Sensor Data

*F.P. Ferrie, G. Soucy, P. Whaite*
In order to perform tasks such as describing, manipulating, and avoiding
collisions with objects, one needs to be able to extract basic information
about their three-dimensional size and shape from available sensor data.
But these data provide only indirect information about 3-D shape, e.g.
how surfaces reflect light or estimated point samples from a surface. Our
related work in visual reconstruction deals explicitly with the problem
of inferring 3-D surfaces from data provided by television cameras and
laser rangefinders. The emphasis of this research, on the other hand, is
the problem of *interpreting* the shape of an object that is represented
by a particular surface. For the purposes of our work the shape of an object
is represented by a collection of volumetric primitives, where each describes
the coarse geometric characteristics of a particular part. The intent is
to be able to generate a unique description of an object in a bottom-up
fashion using only general constraints about a particular domain of objects.
This kind of description is in itself adequate for many tasks involving
object manipulation in a robotics environment. However, a longer term interest
is to investigate how such descriptions can lead to generic forms of object
recognition. The formal basis of this work is differential geometry, which
contributes to the modeling problem in two related ways. First it provides
a mathematical basis for characterizing surface features related to object/ground
separation and parts decomposition. In another related project we are investigating
the structure of these local features or ``trace points'', particularly
how they give rise to contours that separate a surface into different parts.
Second, concepts from differential geometry *in the large* are used
to infer the geometric structure of surfaces corresponding to parts, or
equivalently, to choose a volumetric primitive that best describes a surface
from a finite repertoire. We have developed algorithms for parts decomposition
and the inference of volumetric primitives from surface data. These have
been successfully applied to objects whose parts decomposition can be characterized
by a dense covering of trace points. However, the general case is more
complex and involves cues that must themselves be inferred (similar to
the case of subjective contours). We are continuing in this direction,
but are also looking at applications in machine vision to gain insight
into the general problem.

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*Annual Report*

*Fri Nov 26 23:00:32 GMT 1999*