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Model Globally, Match Locally: Efﬁcient and Robust 3D Object Recognition

Bertram Drost

1

, Markus Ulrich

1

, Nassir Navab

2

, Slobodan Ilic

2

1

MVTec Software GmbH

Neherstraße 1, 81675 Munich, Germany

2

Department of Computer Science, Technical University of Munich (TUM)

Boltzmannstraße 3, 85748 Garching, Germany

{drost,ulrich}@mvtec.com, navab@cs.tum.edu, Slobodan.Ilic@in.tum.de

Abstract

This paper addresses the problem of recognizing free-

form 3D objects in point clouds. Compared to traditional

approaches based on point descriptors, which depend on lo-

cal information around points, we propose a novel method

that creates a global model description based on oriented

point pair features and matches that model locally using a

fast voting scheme. The global model description consists

of all model point pair features and represents a mapping

from the point pair feature space to the model, where simi-

lar features on the model are grouped together. Such repre-

sentation allows using much sparser object and scene point

clouds, resulting in very fast performance. Recognition is

done locally using an efﬁcient voting scheme on a reduced

two-dimensional search space.

We demonstrate the efﬁciency of our approach and show

its high recognition performance in the case of noise, clut-

ter and partial occlusions. Compared to state of the art ap-

proaches we achieve better recognition rates, and demon-

strate that with a slight or even no sacriﬁce of the recogni-

tion performance our method is much faster then the current

state of the art approaches.

1. Introduction

The recognition of free-form objects in 3D data obtained

by different sensors, such as laser scans, TOF cameras and

stereo systems, has been widely studied in computer vi-

sion [2, 9, 12]. Global approaches [8, 13, 14, 18, 23, 25]

are typically neither very precise nor fast, and are limited

mainly to the classiﬁcation and recognition of objects of

certain type. By contrast, local approaches that are based

on local invariant features [1, 4, 5, 6, 7, 10, 15, 17, 19, 20,

21, 24] became extremely popular and proved to be quite

efﬁcient. However, deﬁning local invariant features heavily

Figure 1. Example of two partly occluded instances of an object

found in a noisy, cluttered scene. The matched objects are shown

as red and green wireframe and might not be recognizeable in B/W

copies.

depends on local surface information that is directly related

to the quality and resolution of the acquired and model data.

In contrast to the approaches outlined above we propose

a method that creates a global model description using an

oriented point pair feature and matches it by using a fast

voting scheme. The point pair feature describes the rela-

tive position and orientation of two oriented points as de-

scribed in Sec. 3.1. The global model description consists

of all model point pair features and represents a mapping

from the feature space to the model, where similar features

on the model are grouped together. Such a representation

provides a global distribution of all point pair features on

the model surface. Compared to the local methods, which

require dense local information, our approach allows the

model and the scene data to be represented only by a sparse

set of oriented points that can easily be computed from the

input data. Using sparse data also allows for an important

increase in the recognition speed, without signiﬁcant de-

crease in the recognition rate. A fast voting scheme, similar

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2024-03-06

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