14 research outputs found
A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes
During the last years a wide range of algorithms
and devices have been made available to easily acquire range
images. The increasing abundance of depth data boosts
the need for reliable and unsupervised analysis techniques,
spanning from part registration to automated segmentation.
In this context, we focus on the recognition of known objects
in cluttered and incomplete 3D scans. Locating and fitting a
model to a scene are very important tasks in many scenarios
such as industrial inspection, scene understanding, medical
imaging and even gaming. For this reason, these problems
have been addressed extensively in the literature. Several
of the proposed methods adopt local descriptor-based
approaches, while a number of hurdles still hinder the use
of global techniques. In this paper we offer a different
perspective on the topic: We adopt an evolutionary selection
algorithm that seeks global agreement among surface points,
while operating at a local level. The approach effectively
extends the scope of local descriptors by actively selecting
correspondences that satisfy global consistency constraints,
allowing us to attack a more challenging scenario where
model and scene have different, unknown scales. This leads
to a novel and very effective pipeline for 3D object recognition,
which is validated with an extensive set of experiment
Localized Manifold Harmonics for Spectral Shape Analysis
The use of Laplacian eigenfunctions is ubiquitous in a wide range of computer graphics and geometry processing applications. In particular, Laplacian eigenbases allow generalizing the classical Fourier analysis to manifolds. A key drawback of such bases is their inherently global nature, as the Laplacian eigenfunctions carry geometric and topological structure of the entire manifold. In this paper, we introduce a new framework for local spectral shape analysis. We show how to efficiently construct localized orthogonal bases by solving an optimization problem that in turn can be posed as the eigendecomposition of a new operator obtained by a modification of the standard Laplacian. We study the theoretical and computational aspects of the proposed framework and showcase our new construction on the classical problems of shape approximation and correspondence. We obtain significant improvement compared to classical Laplacian eigenbases as well as other alternatives for constructing localized bases
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
Recognizing 3D objects in the presence of noise, varying mesh resolution,
occlusion and clutter is a very challenging task. This paper presents a novel
method named Rotational Projection Statistics (RoPS). It has three major
modules: Local Reference Frame (LRF) definition, RoPS feature description and
3D object recognition. We propose a novel technique to define the LRF by
calculating the scatter matrix of all points lying on the local surface. RoPS
feature descriptors are obtained by rotationally projecting the neighboring
points of a feature point onto 2D planes and calculating a set of statistics
(including low-order central moments and entropy) of the distribution of these
projected points. Using the proposed LRF and RoPS descriptor, we present a
hierarchical 3D object recognition algorithm. The performance of the proposed
LRF, RoPS descriptor and object recognition algorithm was rigorously tested on
a number of popular and publicly available datasets. Our proposed techniques
exhibited superior performance compared to existing techniques. We also showed
that our method is robust with respect to noise and varying mesh resolution.
Our RoPS based algorithm achieved recognition rates of 100%, 98.9%, 95.4% and
96.0% respectively when tested on the Bologna, UWA, Queen's and Ca' Foscari
Venezia Datasets.Comment: The final publication is available at link.springer.com International
Journal of Computer Vision 201