An Experimental Benchmark for Point Set Coarse Matching
Ferran Roure
1
, Yago D
´
ıez
1
, Xavier Llad
´
o
1
, Josep Forest
1
, Tomislav Pribanic
2
and Joaquim Salvi
1
1
ViCOROB Research Group, University of Girona, Girona, Spain
2
Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
Keywords:
Coarse Matching, Algorithm Standardization, Datasets, Benchmark, 3D Registration.
Abstract:
Coarse Matching of point clouds is a fundamental problem in a variety of computer vision applications. While
many algorithms have been developed in recent years to address its different aspects, the lack of unified mea-
sures and commonly agreed upon data hampers algorithm performances comparison. Additionally, a large
number of contributions are tested only with synthetic or processed data. This is a problem as the resulting
scenario is somewhat less challenging and does not always conform to practical application conditions. In
this paper, we present a new, publicly available database that aims at overcoming the existing problems, pro-
vide researchers with a useful tool to compare new contributions to existing ones and represent a step towards
standardization. The database contains both processed and unprocessed data with attention to specially chal-
lenging datasets. It also includes information on correct solution, presence of noise, overlap percentages and
additional information that will allow researchers to focus only on specific parts of the matching pipeline.
1 INTRODUCTION
3D registration
1
is a fundamental problem in a variety
of areas such as medical imaging, heritage reconstruc-
tion or shape retrieval. Specific applications include
the alignment of temporal 3D images for lesion moni-
toring, the modelling of structures, the reconstruction
of an object giving several views or the bin picking
problem.
While the ICP algorithm (Besl and McKay, 1992)
(Rusinkiewicz and Levoy, 2001) has been adopted as
a ”de facto” standard for the problem of ”fine match-
ing” (i.e. determining the best match between two
sets once an adequate initial approximation has been
found), finding that initial approximation remains an
active research field known as ”coarse matching”.
The coarse matching problem encompasses differ-
ent communities and contributions appear steadily
in all the steps of the matching pipeline (Fig.1): 1)
Detection and Description (Bronstein, 2010) (Salti
et al., 2011) (Yu et al., 2013) 2) Searching Strategies
(Gelfand et al., 2005) (Aiger et al., 2008) (Albarelli
et al., 2010) or 3) Fine matching (Besl and McKay,
1992).
Given the diverse origin of the contribution to
1
Note that we understand the words ”registration”,
”matching” and ”alignment” as synonyms, and we use them
interchangeably throughout the paper.
this research field as well as the divergence in fo-
cus between papers dealing with separate parts of
the matching pipeline, most contributions are eval-
uated with particular datasets that are not accessible
to the research community. Within this data, we dis-
tinguish two types: synthetic (or processed) and real
data. The former usually consists of scanned objects
that have been de-noised, smoothed or similarly post-
processed. The later consist of scanned objects with-
out any such post-processing. This distinction is cru-
cial when evaluating the performance of algorithms,
as data with noise or a low degree of overlap presents
a much more challenging problem.
Some publicly available datasets do exist (Bron-
stein et al., 2008) (Bronstein, 2010) (Bogo et al.,
2014) or the Stanford Repository
2
and some of them
are widely used by the community. However, not
much background information is available for result
comparison. Specifically: 1) No ”correct output”
sets are provided. 2) Similarly, no assessment on the
level of noise or on the final overlap to be achieved
between sets is given 3) No intermediate data con-
cerning the different steps of the matching pipeline is
included. For example, researchers developing new
search strategies (at the later part of the matching
pipeline) need to either first implement state-of-the-
art descriptors or not use them at all.
2
http://graphics.stanford.edu/data/3Dscanrep/
679
Roure F., Díez Y., Lladó X., Forest J., Pribanic T. and Salvi J..
An Experimental Benchmark for Point Set Coarse Matching.
DOI: 10.5220/0005361306790685
In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 679-685
ISBN: 978-989-758-089-5
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Point Registration Pipeline.
In this work we present a new, publicly available,
database that aims at overcoming these limitations
and provide a valuable tools for researchers working
in the coarse matching field. The main characteristics
of our database are:
• It contains datasets targeting different aspects of
the matching problems. Special attention is given
to noise and overlap.
• Proposed solutions are included as well as mea-
sures on the quality of final registration (overlap
percentage and residue).
• The data it contains makes it possible to test dif-
ferent parts of the pipeline separately. For exam-
ple, descriptor data is provided as well as output
after ICP execution.
• Several practical applications and problems are
targeted, so, for example, we include, as well as
the usual data were rigid motion needs to be de-
termined, sets where the rotation is provided sepa-
rately. This makes it possible to test methods that
determine the two parts of the motion separately
(Larkins et al., 2012).
• Data from real application problems allow to test
algorithms in increasingly challenging scenarios.
• It can be accessed online at: http://eia.udg.edu/
3dbenchmark
2 OVERVIEW ON THE STATE OF
THE ART OF 3D
REGISTRATION
New acquisition techniques provide higher-resolution
scanned objects. This results in point clouds that
represent object surfaces more precisely but use also
larger point cloud that demand more efficient algo-
rithms. Many contributions exists in the literature tar-
geting different parts of the matching pipeline (Fig-
ure 1). In terms of published papers, Detectors and
Descriptors are the most active field. Some examples
of the most used methods are Spin Image (Johnson,
1997), SHOT (Tombari et al., 2010), Heat Kernel Sig-
nature (HKS) (Sun et al., 2009), Intrinsic Shape Sig-
natures (ISS) (Zhong, 2009), Fast Point Feature His-
togram (FPFH) (Rusu et al., 2009) or Integral Invari-
ants (Manay et al., 2004).
Most methods in the literature are tested with in-
house data, making it difficult to compare their perfor-
mances to that of new contributions. Moreover, com-
parison to other approaches in the state of the art is
often not provided. Noticeable exceptions to this last
point are (Bronstein, 2010), (Salti et al., 2011) or (Du-
tagaci et al., 2012), which provide meaningful com-
parison among several methods. Furthermore, in most
papers the data used has been processed to filter noise
and outliers. While this makes it possible to obtain
better results and enhance the range of applicability
of algorithms, such post-processes are often not avail-
able in real application situations. For example, the
Heat Kernel HKS descriptor (Sun et al., 2009) shows
high repeatability and distinctiveness with processed
models (Bronstein, 2010), yet with laser-scan data or
image-based reconstructions, it is deemed too selec-
tive to be considered a robust registration algorithm
(Kim and Hilton, 2013). Other methods that have
been extensively tested with real application data are
the Intrinsic Shape Signatures (ISS) (Zhong, 2009)
and Key-Point Quality (KPQ) (Mian et al., 2010).
While their performances with real data are, once
more, worse than with processed data, they still man-
age to obtain good results even with real data (70%
detector repeatability, for example).
Perhaps the most significant contributions in terms
of computational gain made recently correspond to
searching strategies (Aiger et al., 2008) (Mellado
et al., 2014) although the main limitation here is the
lack of comparison between different approaches. Fi-
nally, although research in fine matching is still going
on, the Iterative Closest Point (ICP) algorithm (Besl
and McKay, 1992) has become a ”de-facto” standard
for the problem and obtains acceptable results in a
variety of situations. In the following sections, we
present a new database aimed at helping future re-
searchers overcome these limitations of the current
state of the art.
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
680
3 DATA BASE DESCRIPTION
The database contains several models that cover a va-
riety of scenarios in 3D coarse registration. From low
to high complexity, we provide two processed mod-
els and two ”real application” models. The processed
models are modified versions of the well known Bud-
dha and Bunny models from the Stanford Reposi-
tory
3
. We include five of the original views for ev-
ery model as well as data concerning correct align-
ment results between them. Additionally, we present
modified versions of the Bunny dataset with varying
levels of gaussian noise added. Concerning the real
datasets, the first one corresponds to five views of a
bust model reconstructed using a structured-light sys-
tem (Pribani
´
c et al., 2010). This datasets is more chal-
lenging due to noise in acquisition and includes the
possibility of determining the rotation and the transla-
tion part of the motion separately. Finally, the Joints
dataset present a particular and challenging problem
of interest in industrial settings. The data presented
corresponds to an unstructured heap of manufactured
parts along with a model part that has to be located
(possibly many times over) in the heap. In the re-
mainder of this section we describe the general layout
of the database and all the characteristics shared by
all datasets. In the following subsections we provide
specific details on each particular datasets.
All models are provided in *.ply format. More-
over, we added the normal vectors computed at every
point. We provide this information in order to offer
a common starting point for algorithms that use nor-
mals as their main geometric primitive. Such algo-
rithms include descriptor algorithms such as (John-
son, 1997) (Tombari et al., 2010) but also algorithms
that use Fourier analysis of normal distribution to de-
termine matching (Larkins et al., 2012). Additionally,
all models include:
• Five consecutive views in .*ply format with differ-
ent overlapping ratios. For each view, we provide
the non-aligned view as well as its properly align-
ment pose. All computations were performed au-
tomatically and re-checked manually.
• 4x4 transformation matrices in homogeneous co-
ordinates to align all views.
• Alignment residue computed using the Root
Mean Squared Distance (RMSD) criterion.
• Overlap ration for correctly aligned view. This
is computed as the percentage of paired points
after coarse matching and ICP were successfully
run. A point was considered matched if its near-
est neighbour in the other set was closer than
3
http://graphics.stanford.edu/data/3Dscanrep/
2 × MMD where MMD stands for the mean near-
est neighbour distance for the set.
• All this information is publicly accessible online
at http://eia.udg.edu/3dbenchmark.
3.1 Processed Data
Two of the most well-known objects in the literature
are the Bunny and Buddha models the from Stanford
Repository. Both original datasets consist of several
views with smoothed surfaces. These objects also
appear to have undergone noise and outlier filtering.
This type of data presents less challenging problems
that other datasets.
The Bunny model is the simplest model with ≈
37000 points per view (Figure 2). All features are
clearly defined, without noise, outliers or symmetries.
Pairwise views present decreasing overlap increasing
the difficulty of registration. Specifically, views bun0-
bun1 have ≈ 90% of overlap while views bun3-bun4
have only ≈ 40% (see Table 1 for details).
Figure 2: Left: bun1 view. Right: Detail of bun1 view.
Notice that there are no noise or outliers.
For this model we also include a four-level noisy
versions of three different views for more challeng-
ing tests (Figure 4). Gaussian noise was added to
all the views with varying modulus. This modulus
of the noise vector associated to each point was ran-
domly chosen but had a limit that changed for each
set. Specifically, in the first view this maximum was
set to 1 × MMD. Values of 2 × MMD, 3 × MMD and
4 × MMD were also considered in order to make up
the three remaining modified views.
The Buddha model is a bit more challenging than
the Bunny because of its bigger size (≈ 75000 points
per view). Additionally, it has much smaller details
and a higher degree of symmetry. These represent
challenges both for Descriptor functions as well as
for Searching Strategies. Furthermore, the base of
the figure is a rounded pedestal which induces quite
a noticeable source of symmetry and hampers normal
space analysis.
AnExperimentalBenchmarkforPointSetCoarseMatching
681
Figure 3: Left: bun0 view with 1 ×MMD of gaussian noise.
Right: bun0 view with 4 × MMD of gausian noise.
Figure 4: Left: buddha0 view of Buddha model. Right:
Detail of buddha0 view.
Table 1: Example of data base results from processed data
with a MMD ≈ 5× 10
−4
for Bunny yhe model and MMD ≈
3 × 10
−4
3 for the Buddha model.
Views Residue Ovlp A-B Ovlp B-A
Bunny
0 - 1 3 × 10
−4
91.66% 89.91%
1 - 2 3 × 10
−4
48.67% 79.07%
2 - 3 5 × 10
−4
44.45% 27.53%
3 - 4 4 × 10
−4
38.45% 48.30%
Buddha
0 - 1 2 × 10
−4
79.74% 85.16%
1 - 2 2 × 10
−4
80.04% 89.28%
2 - 3 2 × 10
−4
76.99% 90.84%
3 - 4 2 × 10
−4
75.64% 79.10%
3.2 Real Data
Our real scanned data consist of a set of views from
two different models that have been acquired using
different techniques. The Bust model is a real-sized
mannequin of a human body, scanned with a 3D
structured-light system (Pribani
´
c et al., 2010; Prib-
anic et al., 2013). The Joints model consist on an un-
sorted lot of metal joints acquired using a range scan
(a laser and a single camera). This particular model
was conceived for solving the bin picking problem,
where a robot is expected to identify a certain part
from a stack of unsorted similar parts. Figures 5 and
6 present example views of these datasets.
Both datasets represent a more challenging sce-
nario for any registration method, due to the presence
of noise, the low overlapping ratios and the outliers.
The Bust model was acquired using a structured
light system that consist on projecting a pattern on a
real object and tacking a photo of the scene. Then, the
differences between the original pattern and the cap-
tured one provide the 3D information. The views of
this model contain ≈ 450000 points. There was no
post-processing step and the noise comes from acqui-
sition system (Figure 5). The overlapping ratio is ≈
60%, depending on the view. All the characteristics
of this model (noise, overlap and number of points)
represents a challenging problem for registration al-
gorithms.
Figure 5: Left: bust0 view of Bust model. Right: Detail of
bust0 view.
For this particular case we also provide a single
translation version of data, where an approximation
of the rotation is already computed using a gyro-
scope added to the scanning system (Pribanic et al.,
2013). This approximation reduces the final com-
putation costs, because only a single correspondence
needs to be found. As the estimation of the rota-
tion is noisy due to the nature of the sensor used,
this datasets aim at being useful for researchers who
tackle the matching problem by determining rotations
and translations separately (Larkins et al., 2012). The
data provided allows to compare rotation determina-
tion algorithms to the data obtained by the sensor and
also to prove the usefulness of robust translation de-
termination algorithm with noisy rotation data.
The most complex registration problem in the
database is the Joints model (Figure 6). This scenario
is intended for industrial applications such as quality
control. Specifically, it is an instance of the ”bin pick-
ing” problem, where a robot arm is expected to pick
an industrial part from an unstructured heap of pos-
sibly defective similar parts. This data was obtained
using a range scan composed by a laser and a single
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
682
camera and presents abundant noise and outliers. We
provide this model without any post-processing in or-
der to offer a registration problem as close as possible
to real application conditions. The dataset consists
of a big heap of equal metal pieces, unsorted inside
a box. As well as an ”ideal” model of the piece to
be found within the heap. Although, many possible
matches are possible the noise and the outliers make it
a very difficult task. Providing a scoring function that
ranks all possible matching candidates is also an inter-
ested associate problem. Table 4 presents a summary
of the alignment data obtained for the real datasets.
Figure 6: Left: Single joint1 view. Right: Heap of unsorted
joints.
Table 2: Example of data base results from real data with a
MMD ≈ 0.59 for Bust model and MMD ≈ 0.16 for Joints
model.
Views Residue Ovlp A-B Ovlp B-A
Bust
0 - 1 0.68 86.34% 53.94%
1 - 2 0.66 72.28% 57.31%
2 - 3 0.65 61.16% 60.06%
3 - 4 0.66 61.69% 76.50%
Joints
heap - 1 0.14 4.27% 86.92%
heap - 2 0.19 3.73% 73.60%
4 EXAMPLES OF APPLICATION
The main aim of this database is to be used to compare
new matching algorithms, but it can also be used to
study aspect of the coarse matching problem. In this
section we illustrate how the presented database can
be used to study commonly agreed upon ”truths” of
coarse matching algorithms.
4.1 Application 1: ICP Needs a ”Good
Enough Initial Pose” to Succeed
The very distinction between coarse and fine match-
ing algorithms is based on the accepted fact that ICP
fails to converge or stalls at a local minimum if the ini-
tial pose it is provided with is ”not good enough”. It
Table 3: Table of ICP test. An asterisk indicates that the
algorithm stalled at a local minimum. The dash indicates
that the ICP was not able to find any solution.
Views Rot. Axis Deg. fail Ovlp B-A
Bunny
0 - 1
X 50
◦
14.24%*
Y 70
◦
12.78%*
Z 40
◦
17.45%*
XYZ 25
◦
9.98%*
1 - 2
X 40
◦
-
Y 60
◦
-
Z 30
◦
4.21%*
XYZ 20
◦
4.12%*
Buddha
0 - 1
X 20
◦
-
Y 10
◦
-
Z 10
◦
-
XYZ 2
◦
-
1 - 2
X 15
◦
-
Y 15
◦
-
Z 15
◦
-
XYZ 3
◦
-
is, however, infrequent to see quantifications of what
a local minimum looks like or exactly how good the
initial pose needs to be.
In this experiment we run ICP with the Bunny
and Bust datasets. For each of the two objects, one
view was registered against its consecutive view. The
original pose stands for the best possible initial align-
ment. We then perturbed this initial pose by rotating
the second view along one of the three axes. We re-
peated the process independently for axes X,Y and
Z and also by rotating along all the axes at the same
time. Table 3 contains the summary of this experi-
ment. Column Deg. fail shows the angle (in degrees)
were ICP failed to converge to the global minimum
for the first time. The table also shows how in some
executions, ICP fell in local minimum (indicated in
the table by an asterisk) while in other executions ICP
was not able to converge. Notice that, once again, we
were able to observe differences in behavior for pro-
cessed and real data. Specifically, the Bunny dataset
was much more robust to the perturbation of the ini-
tial pose than the Bust dataset. We also observe, how
the resilience against this type of rotational perturba-
tion increases when the total overlap between views
is higher. As showed in Table 1, the overlap between
bun0 and bun1 is higher (≈ 91%) than bun1 and bun2
(≈ 48%).
4.2 Application 2: Descriptor
Performance Drops in the Presence
of Noise
It is often claimed that noise in data negatively affects
AnExperimentalBenchmarkforPointSetCoarseMatching
683
the behaviour of shape descriptors. In this experiment
we aimed at determining how much noise in data it
takes to get well-established descriptor to fail. Specif-
ically, we chose the well-established SHOT method
(Tombari et al., 2010). We then run a search based on
coupling points according to their descriptor value:
• A three point basis in the first set was chosen.
• Each point in the basis was tentatively matched
to k neighbours in order of decreasing descriptor
similarity.
• Once three correspondences were determined,
distances between the points in the two basis were
checked for consistency.
• If the two basis presented similar distances, then a
rigid motion between the two sets was computed.
• ICP was used to complete the matching process.
The percentage of matched points (also referred
to as overlap percentage) and the residue between
the two sets was computed.
In order to test the effect of noise, we use the sets
with increasing quantity of noise described in section
3.1. Table 4 presents the results obtained.
Table 4: Results of registration process with SHOT descrip-
tor without ICP refinement. Timeout was set at 15 hours.
The overlap presented is the best obtained (at the end of ex-
ecution or at timeout). An asterisk indicates that the overlap
obtained was not the best possible and, thus, the algorithm
stalled at a local minimum.
Noise Residue Ovlp A-B k
- 7 × 10
−4
97.32% 500
1 × MMD 5 × 10
−4
94.58% 500
2 × MMD 1 × 10
−3
36.72%* 500
3 × MMD 1 × 10
−3
21.59%* 500
4 × MMD 1 × 10
−3
22.10%* 500
The results show how in the absence of noise the
descriptors-based search is able to find correspon-
dences very quickly while achieving the total degree
of overlap. Descriptors manage to discriminate points
very well and we only need to consider a low num-
ber of possible correspondences k in order to obtain
the best possible matching. As soon as noise is added
to the data the behaviour of the search suffers. For
the less noisy set, the algorithm still manages to find
the correct matching but needs to consider many more
correspondences. For the remaining sets, containing
more noise, the algorithm was allowed to run for 15
hours before being stopped. During all that time even
when considering a very high number of correspon-
dences, only local minimum were reached and the al-
gorithm was unable to output the correct alignment
for any of the three sets.
5 CONCLUSIONS AND FUTURE
WORK
In this paper we have introduced a new database
aimed at providing researchers in the coarse match-
ing research field with a usable tool that overcomes
some of the current limitations in the field while pro-
viding insight in a variety of aspect of the problem.
Some of the aspects that the database focuses on are:
Providing correct registration results for the publicly
accessible data, with special attention to overlap per-
centages between sets and amount of noise present
in data. Including intermediate data such as surface
normals, descriptor values or separate values for ro-
tations and translations (coming from realistic hard-
ware sources). Finally, the fact that part of the data
comes from realistic applications such as surface re-
construction (Bust model) or industrial applications
(Joints model) aims at providing a benchmark for re-
searchers to show the potential of new contributions
to the field in specially challenging scenarios.
Regarding future work, we expect to increase the
number of models in the database as well as include
outputs from existing and future state of the art algo-
rithms.
REFERENCES
Aiger, D., Mitra, N. J., and Cohen-Or, D. (2008). 4-points
congruent sets for robust pairwise surface registra-
tion. In ACM Transactions on Graphics, volume 27,
page 85.
Albarelli, A., Rodola, E., and Torsello, A. (2010). A game-
theoretic approach to fine surface registration without
initial motion estimation. In Computer Vision and Pat-
tern Recognition (CVPR), 2010 IEEE Conference on,
pages 430–437. IEEE.
Besl, P. J. and McKay, N. D. (1992). A method for regis-
tration of 3-d shapes. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 14(2):239–256.
Bogo, F., Romero, J., Loper, M., and Black, M. J. (2014).
FAUST: Dataset and evaluation for 3D mesh regis-
tration. In Proceedings IEEE Conf. on Computer
Vision and Pattern Recognition (CVPR), Piscataway,
NJ, USA. IEEE.
Bronstein, A. e. (2010). Shrec 2010: robust feature detec-
tion and description benchmark. Eurographics Work-
shop on 3D Object Retrieval, 2(5):6.
Bronstein, A. M., Bronstein, M. M., and Kimmel, R.
(2008). Numerical geometry of non-rigid shapes.
Springer.
Dutagaci, H., Cheung, C. P., and Godil, A. (2012). Eval-
uation of 3d interest point detection techniques via
human-generated ground truth. The Visual Computer,
28(9):901–917.
VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications
684
Gelfand, N., Mitra, N. J., Guibas, L. J., and Pottmann, H.
(2005). Robust global registration. In Eurographics
Symposium on Geometry Processing, pages 197–206.
Johnson, A. E. (1997). Spin-images: A representation for
3-D surface matching. PhD thesis, Citeseer.
Kim, H. and Hilton, A. (2013). Evaluation of 3d feature
descriptors for multi-modal data registration.
Larkins, R. L., Cree, M. J., and Dorrington, A. A. (2012).
Verification of multi-view point-cloud registration for
spherical harmonic cross-correlation. In Proceedings
of the 27th Conference on Image and Vision Comput-
ing New Zealand, pages 358–363. ACM.
Manay, S., Hong, B.-W., Yezzi, A., and Soatto, S. (2004).
Integral invariant signatures. European Conf. on Com-
puter Vision, pages 87–99.
Mellado, N., Aiger, D., and Mitra, N. J. (2014). Super 4pcs
fast global pointcloud registration via smart indexing.
In Computer Graphics Forum, volume 33, pages 205–
215. Wiley Online Library.
Mian, A., Bennamoun, M., and Owens, R. (2010). On the
repeatability and quality of keypoints for local feature-
based 3d object retrieval from cluttered scenes. Inter-
national Journal of Computer Vision, 89(2):348–361.
Pribanic, T., Diez, Y., Fernandez, S., and Salvi, J. (2013).
An efficient method for surface registration. In VIS-
APP (1), pages 500–503.
Pribani
´
c, T., Mrvo
ˇ
s, S., and Salvi, J. (2010). Efficient mul-
tiple phase shift patterns for dense 3d acquisition in
structured light scanning. Image and Vision Comput-
ing, 28(8):1255–1266.
Rusinkiewicz, S. and Levoy, M. (2001). Efficient variants of
the icp algorithm. In IEEE International Conference
on 3D Digital Imaging and Modeling, pages 145–152.
Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast
point feature histograms (fpfh) for 3d registration. In
Robotics and Automation, 2009. ICRA’09. IEEE In-
ternational Conference on, pages 3212–3217.
Salti, S., Tombari, F., and Stefano, L. D. (2011). A perfor-
mance evaluation of 3d keypoint detectors. In IEEE
International Conference on 3D Imaging, Modeling,
Processing, Visualization and Transmission, pages
236–243.
Sun, J., Ovsjanikov, M., and Guibas, L. (2009). A concise
and provably informative multi-scale signature based
on heat diffusion. In Computer Graphics Forum, vol-
ume 28, pages 1383–1392.
Tombari, F., Salti, S., and Di Stefano, L. (2010). Unique
signatures of histograms for local surface description.
European Conf. on Computer Vision, pages 356–369.
Yu, T.-H., Woodford, O. J., and Cipolla, R. (2013). A per-
formance evaluation of volumetric 3d interest point
detectors. International Journal of Computer Vision,
pages 1–18.
Zhong, Y. (2009). Intrinsic shape signatures: A shape
descriptor for 3d object recognition. In IEEE Inter-
national Conference on Computer Vision Workshops,
pages 689–696.
AnExperimentalBenchmarkforPointSetCoarseMatching
685
