Eurographics Workshop on 3D Object Retrieval (2017)
I. Pratikakis, F. Dupont, and M. Ovsjanikov (Editors)

SHREC’17 Track
Large-Scale 3D Shape Retrieval from ShapeNet Core55
Manolis Savva1∗ , Fisher Yu2∗ , Hao Su3∗ , Asako Kanezaki3 , Takahiko Furuya4 , Ryutarou Ohbuchi4 , Zhichao Zhou5 , Rui Yu5 , Song Bai5
Xiang Bai5 , Masaki Aono6 , Atsushi Tatsuma6 , S. Thermos7 , A. Axenopoulos7 , G. Th. Papadopoulos7 , P. Daras7
Xiao Deng8 , Zhouhui Lian8 , Bo Li9 , Henry Johan10 and Yijuan Lu11 , Sanjeev Mk 12
1

Princeton University, USA
2
UC Berkeley, USA
2
Stanford University, USA
3
National Institute of Advanced Industrial Science and Technology (AIST), Japan
4
University of Yamanashi, Japan
5
School of Electronic Information and Communications, Huazhong University of Science and Technology, China
6
Toyohashi University of Technology, Japan
7
Information Technologies Institute, Centre for Research and Technology Hellas, Greece
8Institude of Computer Science and Technology, Peking University, Beijing, China
9
School of Computing, University of Southern Mississippi, USA
10
Visual Computing, Fraunhofer IDM@NTU, Singapore
11
Department of Computer Science, Texas State University, San Marcos, USA
12
Indian Institute of Technology, Bombay∗
Track organizers (contact email: shrecshapenet@gmail.com)

Abstract
With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly
prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset. It is acontinuation of the SHREC 2016 large-scale shape retrieval challenge with a goal of measuring progress with recent developments in deep learning methods for shape retrieval. We use ShapeNet Core55, which provides more than 50 thousands models
over 55 common categories in total for training and evaluating several algorithms. Eight participating teams have submitted
a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. The approaches vary in terms of the 3D representation, using multi-view projections, point sets, volumetric grids, or traditional 3D
shape descriptors. Overall performance on the shape retrieval task has improved significantly compared to the iteration of thiscompetition in SHREC 2016. We release all data, results, and evaluation code for the benefit of the community and to catalyze
future research into large-scale 3D shape retrieval (website: https://www.shapenet.org/shrec17).
Categories and Subject Descriptors: H.3.3 [Computer Graphics]: Information Systems- Information Search and Retrieval

1. Introduction
The rapid improvements in 3D modeling tools and the recent rise ofcommodity depth sensors have led to a deluge of 3D content. The
increasing availability of 3D data puts a focus on the developmentof scalable and efficient algorithms for search, retrieval and analysis. Improved methods for 3D shape retrieval have the potential
to enable many applications in virtual and augmented reality, 3D
printing, and inverse graphics for computer vision.
In this track of the yearly SHREC contest we evaluate the perc 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

formance of deep learning methods for 3D shape retrieval. The recent development of large scale repositories of 3D shapes such as
ShapeNet [CFG∗ 15] made available much bigger datasets to develop and evaluate new algorithms. This year we repeat the 3D
shape retrieval challenge organized for SHREC 2016 around theShapeNet dataset. Though retrieval of relevant 3D models given aquery model has been studied for more than a decade, deep learning approaches have only been introduced in the last couple ofyears. Our goal is to evaluate the performance of cutting edge deeplearning 3D shape retrieval methods. Furthermore, we would like

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

to see how much progress has been made since last year, with moremature methods on the same dataset. To that end, we again use theShapeNet Core55 subset of ShapeNet which consists of more than
50 thousand models in 55 common object categories.
Eight participating teams have submitted a variety of retrieval
methods. All methods except one are based on deep learning models. In contrast to last year where all teams used projection-based
input data, this year there were methods using various types of 3D
representation: multi-view projections, point sets, volumetric occupancy grids, and traditional 3D shape features. We evaluate themethods with several standard information retrieval performance
metrics. RotationNet by Kanezaki et al. is projection-based and performs the best on consistently aligned 3D models. DLAN by Furuya et al. is point set–based and performs the best on models withrandom orientations. For both types of data, the best performers thisyear have significantly outperformed the champion methods fromlast year. However, the overall performance still leaves space for
improvement. We hope to further catalyze research into 3D model
retrieval by releasing both the data and evaluation code publicly forthe benefit of the community.

2. Dataset
The ShapeNet Core55 competition dataset contains a total of 51162
3D models categorized into 55 WordNet [Mil95] synsets (lexical
categories belonging to a taxonomy of concepts in the English language). Before using this data for the competition, the models werededuplicated. Furthermore, each model was assigned a sub-synset(sub-category) label which indicates a more refined class for the object (e.g., a model may have a sub-synset of “fighter jet” within thesynset of “airplane”. There are 204 sub-synset labels across the 55
synsets and these are used to establish a gradated relevance score(beyond just matching vs not matching synset label) in one of theevaluation metrics.
The original models in the evaluation dataset were taken from theShapeNet corpus which was collected primarily from the Trimble
3D warehouse [Tri12]. To make training and evaluation of learning based methods possible, we established standard training, validation and test splits of the dataset. The proportions chosen were70% (35764), 10% (5133) and 20% (10265) respectively out of thetotal 51162 models. The ShapeNet model data was converted to
OBJ format with only geometric information, and the model dimensions were normalized to a unit length cube. In addition, since
ShapeNet provides consistent upright and front orientation annotation for all models in the ShapeNetCore corpus, all model data wasconsistently aligned. We call this the “normal” dataset. To establish a more challenging version of the data without the assumption
for pre-existing consistent alignments, we generate a “perturbed”
version of the model data where each model has been randomly
rotated by a uniformly sampled rotation in SO(3). As many deeplearning methods rely on view-based feature computation, the second dataset is a more challenging scenario. All participating methods were evaluated on both datasets.

3. Evaluation
Each split of the dataset (train, val, test) was treated independently
as a query and retrieval corpus. Participants were asked to return aranked list of retrieved models for each model in a given set, where
the target models to be retrieved were all models in that set, including the query model itself. Retrieval ranked lists were required toprovide at most 1000 results in descending order of similarity (orrelevance) to the query model. Although a similarity score is provided for each entry in the submission, it is not used in evaluation.
Each participant method was free to choose whether to return fewer
relevant retrieval results to obtain better evaluation performance.
The ranked lists were evaluated against the ground truth category(synset) and subcategory (subsynset) annotations. An item in the
retrieved lists is positive if it is in the same category with the target
model in the retrieval. Otherwise, it is negative. For each entry in
the lists, we calculate the precision and recall. The precision at an
entry is defined as the percentage of positive items up to this entry.
The recall at an entry is defined as the number of positive items
up to this entry divided by the smaller number between the total
number of objects in the category or maximally allowed retrieved
list length (1000 in this competition). Combining the precision andrecall at each entry, we calculate the F-score.
At each entry, we also calculate normalized discounted cumulative gain (NDCG). The NDCG metric uses a graded relevance: 3
for perfect category and subcategory match in query and retrieval, 2for category and subcategory both being same as the category, 1 forcorrect category and a sibling subcategory, and 0 for no match. Subcategory is only used in NDCG. The simplistic graded relevancewe defined using just categories and subcategories is a somewhat
limited attempt at capturing a human notion of graded relevance
between 3D models. In the near future, the track organizers plan
to collect judgments of retrieved 3D model relevance from people
in order to establish a more proper relevance for retrieved models.
This will allow computation of a more challenging version of the
NDCG evaluation metric which will measure the degree to which
retrieval methods match human notions of relevance/similarity ordering of the 3D models.
In summary, we can calculate four scores at each entry in a
list: precision, recall, F-score and NDCG. In Section 6, we show
precision-recall plots to compare different submitted methods. Besides using average precision to evaluate the quality of each retrieved list, we also use precision, recall, F1 and NDCG at the endof the retrieval list as a summary metric. These metrics are referred
to as P@N, R@N, F1@N and NDCG@N, where the N refers to
the total retrieval list length chosen by the method, and is allowed
to vary across queries. The scores of the lists in a category are combined by taking the average.
In order to combine the retrieval results of different categories,
we use two versions of the above metrics: macro and micro averaged. The macro-averaged version is used to give an unweighted
average over the entire dataset. The retrieval scores for all the models are averaged with equal weights. In the micro-averaged version, each query and retrieval results are treated equally across categories, and therefore the results are averaged without reweighting based on category size. This gives a representative performance
metric average across categories.
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55
view 2view 1

Input:- Mul!-view images ofan unaligned object- Category labelview 3

CNN

car
bed
i _viewcarbed
i_viewcarbed
i_view

CNN

car
bed
i_viewcarbed
i_viewcarbed
i_view

CNN

car
bed
i_viewcarbed
i_viewcarbed
i_viewview 2

i_view i_view car

car

Backpropaga!onview 3i_view car i_view

view 3view 2view 1… view rota!on candidates (1, 2, 3), (2, 3, 1), and (3, 1, 2)

view 1
car i_view i_view

Figure 1: Overview for RotationNet. Illustration of the training process of RotationNet, where the number of views M is 3 and the numberof categories N is 2. A training sample consists of M images of an unaligned object instance and its category label y. For each input image,our CNN (RotationNet) outputs M histograms with N + 1 bins whose norm is 1. The last bin of each histogram represents the “incorrectview” class, which serves as a weight of how likely the histogram does not correspond to each view label. According to the histogram values,
we decide which image corresponds to views 1, 2, and 3. There are three candidates for view rotation: (1, 2, 3), (2, 3, 1), and (3, 1, 2). For
each candidate, we calculate the score for the ground-truth category (“car” in this case) by multiplying the histograms and selecting the best
choice: (2, 3, 1) in this case. Finally, we update the CNN parameters in a standard back-propagation manner with the estimated view labels.

4. Participants
There were eight participating groups in this track. Each group submitted results on both the normal and perturbed versions of the test
data, with one exception of a group that did not submit perturbed
dataset results.
• RotationNet, by A. Kanezaki (referred to as Kanezaki)• GIFT: Learning Deep Shape Similarity via Improved GIFT, byZ.Zhou, S.Bai, R.Yu, X.Bai (referred to as Zhou)• ReVGG: Reduced VGG-M Network and Modified Neighbor Set
Similarity, by A. Tatsuma and M. Aono (referred to as Tatsuma)• DLAN: Deep aggregation of local 3D geometric features, by T.
Furuya and R. Ohbuchi (referred to as Furuya)• MVFusionNet: Multi-view Fusion Network, by S. Thermos, A.
Axenopoulos, G. Th. Papadopoulos and P. Daras (referred to as
Thermos)• CM-CNN: View based 3D Shape Retrieval using Clock Matching and Convolutional Neural Networks, by X. Deng, Z. Lian
(referred to as Deng)• ZFDR: Hybrid Shape Descriptor ZFDR, by B. Li, H. Johan, and
Y. Lu (referred to as Li)• VoxelNet: Deep VoxelNet to extract features from voxel grids,
by S. Mk (referred to as Mk)
5. Methods
In this section we compile the description of methods by each participating group, from the participants’ perspective.
5.1. RotationNet, by A. Kanezaki
Our method is based on a Convolutional Neural Network (CNN)based model “RotationNet” [KMN16]. Similarly to Multi-view
CNN [SMKLM15], it takes multi-view images of an object as input and estimates its object category. Our method treats the posec 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

labels as latent variables, which are optimized to self-align in an unsupervised manner during the training using an unaligned dataset.
The code is available on https://github.com/kanezaki/
rotationnet.
5.1.1. Training of RotationNet
The training process of RotationNet is illustrated in Fig. 1. We assume that multi-view images of each training object instance areobserved from all the pre-defined viewpoints. Let M be the numberof the pre-defined viewpoints and N denote the number of target
object categories. A training sample consists of M images of an
object {xxi }M
i=1 and its category label y ∈ {1, . . . , N}. We attach aview label variable vi ∈ {1, . . . , M} to each image x i and set it to jwhen the image is observed from the j-th viewpoint, i.e., vi ← j.
In our method, only the category label y is given during the training whereas the view labels {vi } are unknown, namely, {vi } are
treated as latent variables that are optimized in the training process. We introduce an “incorrect view” label and append it to the(i)M×(N+1)target category labels. Letting Pi = {p j,k } ∈ R+
denote amatrix composed of P(ŷi | x i , vi ) for all the M viewpoints and N + 1
classes, the target value of Pi in the case that vi is correctly estimated is defined as follows:
1 ( j = vi and k = y) , or(i)p j,k =
(1)
( j 6= vi and k = N + 1)
0 (otherwise).
The parameters of RotationNet are iteratively updated via standard
back-propagation of M softmax losses, whereas {vi }M
i=1 are determined in a manner to maximize the probability that a training sample belongs to its correct category y in each iteration.
We place virtual cameras on the M = 20 vertices of a dodecahedron encompassing the object. There are three different patternsof rotation from a certain view, because three edges are connected

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

1st

3D Shape Database

Shape Representation
2nd
CNN Feature
Extraction
Averagepooling
Projections

View
Selection

Off-line
On-line
CNN Feature
Extraction
Averagepooling
Projections

CosineSimilarity

Re-ranking

View
Selection

Query Shape
Shape Representation

Candidate List

Figure 2: Overview for Improved GIFT. The pipeline of the Improved GIFT method is illustrated.

to each vertex of a dodecahedron. Therefore, the number of candidates for all the view labels {vi }M
i=1 is 60 (= 3M).

5.1.2. Retrieval
Four RotationNet models are trained respectively with (a)train+“normal” set in 55 categories, (b) train+“normal” setin 203 subcategories, (c) train+“perturbed” set in 55 categories, and (d) train+“perturbed” set in 203 subcategories.
We predict category/subcategory labels of all the samples intrain/val/test+“normal” set using models trained with (a) and(b), whereas we use models trained with (c) and (d) for
train/val/test+“perturbed” set. For each query, we construct a retrieval set containing all the samples with the same predicted category label in the order of descending scores of the predicted category. Then we re-rank the results so that the samples with the same
predicted subcategory label are ranked higher than others.

5.2.1. View Feature Extraction
Similar to GIFT [BBZ∗ 16], we utilize Convolution Neural Network(CNN) to extract view features, and fine-tune a pre-trained CNN
model (VGG-S from [CSVZ14a]) using the rendered views. However, different from GIFT which simply assigns the projections to
the label of their corresponding 3D shape, we propose a simple
trick which handles the semantic clutter, i.e., shapes from different
categories may share very similar views at specific view points. In
each batch of one iteration, we randomly select a portion of projections (30 in our experiment) from each 3D shape as the training exemplar, then exert average-pooling to aggregate those projections.
The entire of network is trained using back-propagation with classification loss on the 3D shapes.
In the testing phase, we fed the projections of the testing 3D
shapes to the neural network in the forward direction, and extract
the activations of the 7-th fully-connected layers. The activations
are then L2 normalized, serving as the features of the projections.
5.2.2. Multi-View Matching

5.2. Learning Deep Shape Similarity via Improved GIFT, by
Z. Zhou, R. Yu, S. Bai, X. Bai
We present a view-based shape search engine (see Figure 2)
based on GIFT [BBZ∗ 16], which is composed of four components, i.e., projection rendering, view feature extraction, multiview matching and re-ranking. We render each 3D shape with Nv(Nv = 50 in our experiment) depth images. Compared with the original GIFT, we propose several improvements to boost the retrieval
accuracy and matching speed, detailed below.

Given a query shape xq and a certain shape x p from the database
X = {x1 , x2 , . . . , xN }, we can easily obtain two feature sets V(xq ) ={q1 , q2 , . . . , qNv } and V(x p ) = {p1 , p2 , . . . , pNv } respectively using
the trained neutral network.
5.2.3. Matching Function
In order to compute the pairwise similarity between shapes, we first
get the feature of the 3D shape xq via average-pooling as
Fq =

1
qi .
Nv ∑
i

(2)

c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

and vice versa for the shape x p . Then, the similarity between xq andx p is computed directly using the cosine similarity
S(xq , x p ) = FqT Fp .

(3)

We draw the readers’ attention that the above matching strategy
is equivalent to the modified version of Hausdorff distance, whichsimply averaging all the pairwise similarities between two viewsets, that is,S(xq , x p ) =1∑ ∑ qTi q j .
Nv2 i j(4)

Since set-to-set matching is computational expensive, the original
GIFT [BBZ∗ 16] employed inverted file to accelerate this computation. In this work, we suggest the alternative way presented in
Eq. 2 and Eq. 3, which performs multi-view matching in vector
space model.
5.2.4. Enhance Matching Accuracy
In general, there exist non-discriminative projections, which capture irrelevant information to represent a 3D shape. To further enhance the matching accuracy, we aim at only preserving discriminative projections while discarding those noisy projections. To thisend, we define the degree of discrimination D of i-th view of xq asCjj

Dqi = exp( ∑ cqi · log cqi ),

(5)

j=1

where C denotes the number of categories in the training set (C =j
55 in our experiment), and cqi denotes the classification probabilityof qi to the j-th category. It suggests that we deem the view is discriminative if it has larger value of D (it means the view has a large
probability belonging to only a small number of categories while
unlike to be classified to the rest categories). In our experiment,
we only choose top-40 views with larger discriminative scores, anddiscard the rest views.
5.2.5. Re-ranking
The re-ranking component is the same as GIFT [BBZ∗ 16]. Its principle is to refine the input similarity by comparing the neighbourhood sets of two 3D shapes via the generalized Jacquard distance.
5.3. Reduced VGG-M Network and Modified Neighbor Set
Similarity, by A. Tatsuma and M. Aono
Our approach consists of feature extraction from a Convolutional
Neural Network (CNN) [LBD∗ 89] with reduced number of filters
for depth-buffer images and similarity calculation by an improved
method of Neighbor Set Similarity (NSS) [BBW15].
We extract the feature vector of 3D model by inputting the rendered depth-buffer images to CNN. As a preprocessing, we firstnormalize the position and scale of 3D model. We translate the center of the 3D model to the origin and then normalize the size of the
3D model to the unit sphere. In addition, for the perturbed data set,
we normalize the rotation of 3D model by using Point SVD [TA09].
Next, we rendered 38 depth-buffer images with 224 × 224 resolutions by setting the view point to each vertex of the unit geodesicc 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

Table 1: Reduced VGG-M network configuration.
Layer
input
conv1
conv2
conv3
conv4
conv5
full6
full7
full8

Setup
224 × 224 grayscale image
7 × 7 × 48 stride 2 pool 2 × 2
5 × 5 × 64 stride 2 pool 2 × 2
3 × 3 × 128 stride 1
3 × 3 × 128 stride 1
3 × 3 × 128 stride 1 pool 2 × 21024 dropout
1024 dropout
55 softmax

sphere. Finally, we obtain the feature vector of 3D model by averaging the CNN output vectors, which denote the classification
probability, of each depth buffer image. For the dissimilarity between two feature vectors, we employ the Euclidean distance.
Our CNN configuration is summarized in the Table 1. The network can be seen as a network with reduced number of filters
and units in the VGG-M network [CSVZ14b]. The activation function for all layers except the full8 layer is the REctification Linear
Unit [NH10]. The pooling method for the convolutional layers is
max pooling. We call the network the Reduced VGG-M network(ReVGG). We trained the ReVGG with training dataset classified
to 55 classes.
In the calculation similarity, we propose an improvement methodof NSS, named Modified NSS (MNSS). NSS defines the similarity
between two feature vectors as the average of all the pairwise similarities between two neighbor sets. Let Na and Nb be the sets of
k-nearest neighbors of the feature vector a and b. The NSS SNSS
between a and b is defined as follows:
SNSS (a, b) =1
∑ ∑ s(x, y),k2 x∈Na y∈Nb(6)

where s(x, y) = exp(−d(x, y)2 /2σ2 ) is the similarity, and d(x, y) isthe distance between two feature vectors.
The NSS does not consider the similarity between feature vectors
which are subject to similarity calculation. To capture the structureof data distribution, we think that the similarity between the subject feature vectors is also important. Thus, we define the MNSS
between a and b is as follows:(1
if a = b
SMNSS (a, b) =, (7)αs(a, b) + (1 − α)SNSS (a, b)) otherwise
where α ∈ [0, 1) is the parameter representing the influence degreeof neighbors to the MNSS. In addition, to emphasize neighborhood
relations, MNSS updates the similarity iteratively by repeating the
calculation of similarity in Eq. (7). Since the similarity value withMNSS has the range [0, 1], we obtain the dissimilarity value by
calclulating 1 − SMNSS (a, b).
In our run, we set k to 10, σ to 0.5, α to 0.9, and the numberof iterations in the MNSS to 5. We output models by which thedissimilarity to the query is less than 0.8.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

Convert tooriented
point set

SampleSOIs

Rotateby PCA

Generate
RNGs

Feed into
DLAN

Extract & encode
local geometric features

3D convolution

classification
layer

aggregation
by average
pooling…

…

…

mid-level
local features

aggregatedfeature…

…

…

EE-block

…

…

RNGs

…

oriented
point sets
within SOI

…

…

…

…

…

…

oriented
point set

…

…

…

…

…

3D
model

Aggregate local features &compress aggregated feature

high-level
aggregated feature

AC-block

Deep Local feature Aggregation Network (DLAN)

Figure 3: Overview for DLAN. First half of DLAN, the EE-block, extracts rotation-invariant, mid-level local features from Rotation Normalized Grids (RNGs). The AC-block aggregates the set of mid-level local features into a feature per 3D model by average-pooling. The poolingis followed by deep learning-based dimension reduction to produce a salient and compact feature per 3D model.

5.4. Deep aggregation of local 3D geometric features, by T.
Furuya and R. Ohbuchi
The algorithm aims at extracting 3D shape descriptors that are robust against geometric transformations including translation, uniform scaling, and rotation of 3D models. The algorithm is called
Deep Local feature Aggregation Network (DLAN) [FO16]. DLAN
takes as its input a set of low-level 3D geometric features having invariance against 3D rotation. It produces a compact, high-level descriptor per 3D model for efficient and effective matching among
3D shapes. Figure 1 describes the processing pipeline of DLAN,
which consists of the following four steps.
1) Generating oriented point set: Given a polygon-based 3Dmodel, it is first converted into a 3D oriented point set by sampling
the surfaces of the 3D model by using the algorithm by Osada etal. [OFCD02]. This sampling process gives the DLAN certain invariance against shape representations of 3D model. The algorithm
randomly and uniformly samples points on the surfaces of the 3Dmodel. Each point is associated with the normal vector of the triangle on which the point is sampled. We sample 3k oriented points
per 3D model. The oriented point set of the 3D model is uniformly
scaled to fit a sphere having diameter 1.0.
2) Extracting rotation-invariant local features: We sample a setof Spheres-Of-Interest (SOIs) from the oriented point set of the 3Dmodel. Position and radius of each SOI are chosen randomly. To
make local features invariant against rotation of 3D shapes, each
SOI is rotated to its canonical orientation, which is computed byapplying Principal Component Analysis to points enclosed by the
SOI. The rotation-normalized SOI is then spatially divided by using
3D grids having 7, 6, and 3 grid intervals along the first, second,
and third principal axis, respectively. We refer to the 3D grid as
Rotation-Normalized Grid (RNG). For each cell in the RNG, a 3D
geometric feature POD [FO15] is computed to describe low-level
3D geometric feature within the cell. We extract 100 RNGs per 3Dmodel.
3) Aggregating local features via DLAN: DLAN consists of two
blocks; (1) EE-block performs Extraction and Encoding of rotation

invariant local 3D features, and (2) AC-block performs Aggregation of the local 3D features into a feature per 3D model followed
by Compression of the aggregated feature. The EE-block encodes
each RNG to a mid-level local feature per SOI via two 3D convolution layers followed by three fully-connected layers. The aggregation layer in the AC-block pools, by averaging, the set of encoded
mid-level local features into a single feature per 3D model. The
subsequent three fully-connected layers compress the aggregatedfeature to generate a compact and salient feature per 3D model.
Detailed description of DLAN is found in [FO16].
4) Comparing aggregated features: A compact (i.e., 128dimensional) aggregated feature of the query 3D model is efficiently compared against the aggregated features of the retrieval
target 3D models in the database. Cosine similarity is used to compare a pair of aggregated feature vectors. Retrieval ranking for thequery consists of 3D models whose similarity to the query is larger
than 0.95.
To effectively train the DLAN, we employ two-step training of
the network. That is, the EE-block is pre-trained first by using alarge set of labeled RNGs. Then, the entire network is trained byusing a set of labeled 3D models.
A) Pre-training EE-block: To learn expressive local feature better suited for accurate 3D shape retrieval, we pre-train the EE-blockby using a large number of labeled RNGs so that it could predict
object categories of the RNGs. To do so, we sample RNGs having
random position and scale from the labeled 3D models contained in
the training dataset. The label for each training RNG is identical to
the label for the 3D model from which the RNG is sampled. We collect 2M labeled RNGs from the training dataset. For the supervised
training, we append the classification layer with softmax function at
the output end of the EE-block. Parameters, i.e., connection weight
among neurons, in the EE-block are randomly initialized and crossentropy loss is minimized by using AdaGrad algorithm [DHS11]
with initial learning rate = 0.1. We perform 50% dropout for all thelayers except for the input layer and the output (i.e., classification)
layer. We iterate the pre-training for 100 epochs.
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

Figure 4: Overview for MVFusionNet. Illustration of the pipeline for the Multi-view Fusion Network.

B) Training whole network: After supervised pre-training of
the EE-block, we train the entire network including both the EEblock and the AC-block by using labeled 3D models in the training
dataset. The parameters in EE-block inherits as its initial values theresult of its pre-training. The parameters in AC-block are randomly
initialized. Similar to pre-training the EE-block, training of the entire DLAN is done also by minimizing cross-entropy loss by using
AdaGrad algorithm with initial learning rate = 0.1 and 50% dropout
during the training. We iterate the training for 70 epochs.
5.5. Multi-view Fusion Network, by S. Thermos, A.
Axenopoulos, G. Th. Papadopoulos, P. Daras
The proposed framework fuses the features extracted using a deep
Neural Network (NN) and a multi-view descriptor. The NN and thehand-crafted feature extraction procedure receive rendered views of
3D shapes as input. Fusion of the aforementioned features improves
the 3D shape retrieval.
5.5.1. Pre-processing and feature extraction
The input 3D model may have arbitrary pose. Thus, prior to extraction of 2D views, a rotation estimation step is necessary to ensure that the sequence of 2D views is consistent for all models of
the same category. In the proposed framework, the Combined Pose
Estimation (CPE) method [DAL12] is applied, which intuitively
merges the well-known Continuous Principal Component Analysis(CPCA) with plane symmetry and rectilinearity.
Extraction of hand-crafted features is based on the Compact
Multi-View Descriptor (CMVD), which is presented in [DA09].
After rotation estimation, a set of uniformly distributed views are
extracted. The viewpoints are chosen to lie at the 18 vertices ofa regular 32-hedron. The 2D image types are depth images withpixel values in the range [0, 255]. Three rotation-invariant functionals are applied to each view to produce the descriptors: i)
2D Polar-Fourier Transform, ii) 2D Zernike Moments, and iii)
2D Krawtchouk Moments. The total number of descriptors perview is ND = NFT + NZern + NKraw = 212, where NFT = 78,
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

NZern = 56, and NKraw = 78. For the deep features extraction,
each of the 18 views of a 3D shape is passed through a ResNet[HZRS16] model with 18 layers. A 512-dimensional feature vector is extracted per view. The ResNet-18 network is pre-trained on
ImageNet [RDS∗ 15] and fine-tuned on ShapeNetCore55.
The ShapeNetCore55 benchmark consists of 55 categories,
which are highly imbalanced. In the training set, the smallest category has 39 while the largest one has 5876 samples, respectively.
The subcategories are also imbalanced. In order to balance thecategory distribution, we randomly sample a training set for eachepoch according to a penalized category distribution using the same
method with [SMKL15]. We use t = 0.5 and t = 0.2 as a penalty
factory for category and subcategory distribution respectively.
5.5.2. Feature Fusion and Object recognition
In this section the proposed feature fusion and object recognition
mechanisms are summarised. The 512-dimensioanl deep feature
vector is concatenated with the corresponding 212-dimensional
hand-crafted features for every considered view, as can be seen in
Figure 4. All produced 724-dimensional vectors are stacked andpassed through the Fusion Network. The latter consists of a 2D
convolutional layer with 18 1 × 1 size kernels, a max pooling operator and two 4096-dimensional fully connected layers. The convolutional layer learns the inter-view spatial correlations and thepooling layer adds spatial invariance while propagating the mostdominant activations.
The features extracted from ResNet’s last layer and the handcrafted ones are normalized before the concatenation.
5.5.3. Retrieval
The architecture described above is trained for both the categories
and the subcategories of the dataset, resulting in two separate networks, Network1 and Network2, respectively. At test time, Network1 is used for category prediction, while Network2 is used
for feature extraction, taking the last fully connected layer. Each

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

Figure 5: Overview for CM-VGG. The pipeline of the method is illustrated.

query model is passed through Network1, which predicts its category. Based on this prediction, an initial retrieval set containing all
the models predicted with the same category is constructed. Then,the query and the initial retrieval set are passed through Network2,
which computes the L2-distance between the query and the models, producing the final rank list. Models with L2-distance above acertain threshold are dropped (i.e. misclassified by Network1).
5.6. CM-VGG: View based 3D Shape Retrieval using Clock
Matching and Convolutional Neural Networks, by X.
Deng and Z. LianAs shown in Figure 5, our method consists of the following threesteps:(Clock Matching) algorithm [LGSX13] is utilized to calculate thedissimilarity between two 3D objects by computing the minimum
distance of 24 matching pairs.
For the contest, the number of views we capture around the 3Dmodel is selected as 6 and the subcategory labels are not used forthe training. Since the method is mainly based on Clock Matching,
the VGG CNN model, and utilizes 6 views for each model, we denote it as “CM-VGG55-6DB”. In fact, the performance of our approach can be further improved by increasing the number of views
captured around the 3D model.
5.7. Hybrid Shape Descriptor ZFDR, by B. Li, H. Johan and
Y. Lu [LJ13]

1) Pose Normalization: Given a 3D model which is perturbed byrandom rotations, a pose normalization algorithm based on Principal Component Analysis (PCA) (see [LRS10]) is implemented to
get the normalized pose for the model. Note that for models thathave already been perfectly aligned, this step can be skipped over.
2) Feature Extraction: After pose normalization, we capture themodel’s depth-buffer views on the vertices of a given unit geodesic
sphere whose mass center is also located in the origin. The geodesic
spheres used here are obtained by subdividing the unit regular octahedron(see Figure 5). Based on different times (0 and 2) of subdivision, the numbers of depth-buffer images captured are 6 and66, respectively. During offline period, we train the VGG convolutional neural network with 16 layers proposed in [SZ14] on the
training set whose models are classified into 55 categories by using the source code named MatConvNet [VL15]. Furthermore, we
use parameters of the VGG-16 model pre-trained on the imagenet
database as the initial parameters of the CNN model adopted in our
method. In this way, the modified VGG model can be trained to getsatisfactory performance for this task in just a few hours on a PC
with a Titan X GPU. Online, we extract a 4096-dimensional feature
vector from each view of the 3D model by feeding the depth-buffer
image into the trained CNN and recording the output of featurelayer of the network. To reduce the storage requirement, an efficientfeature coding scheme proposed in [LGSX13] is also adopted. In
this manner, for example, the original feature vector that contains
4096 float numbers can be coded into a feature vector with only
about 1000 integers. Finally, all feature vectors of the views captured around the 3D model are combined sequentially into a single
feature vector that describes the 3D shape.
3) Dissimilarity Calculation: An multi-view shape matching

Figure 6: Overview of ZFDR [LJ13]. Figure illustrates the feature
extraction process.

ZFDR [LJ13] is a non-learning based hybrid shape descriptor, which integrates both visual and geometric information of a
3D model: (1) Thirteen cube-based sampling views’ Zernike moments and Fourier descriptor features; (2) Six standard depth buffer
views’ Depth information features and the 3D model’s Ray-based
features.
The intention of running ZFDR on ShapeNet Core55 is to examine the performance of this typical hybrid 3D shape descriptor on
the latest large-scale 3D model dataset ShapeNet Core55, thus providing a baseline in the aspect of non-learning based approaches.
It will help to find out the performance gap between non-learning
based approaches and learning-based ones, such as all the four deeplearning-based participating methods [SYS∗ 16] in the last track
based on the same ShapeNet Core55 dataset.
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

The overview of ZFDR feature extraction process is illustrated in Figure 6. Continuous Principle Component Analysis(CPCA) [Vra04] is applied for 3D model normalization. Details
about extraction of the four component features Z, F, D and R aredescribed as follows.
1) Visual features: Zernike moments and Fourier descriptors(ZF). (1) Cube-based view sampling: With a consideration for both
efficiency and accuracy, a set of thirteen silhouette feature views
are sampled by setting cameras on a cube’s four top corners, threeadjacent face centers and six middle edge points. (2) Feature extraction: To represent the region-based features of each silhouette
feature view, up to 10th order Zernike moments [KH90] (totally
35 moments) are computed; while to characterize its contour-based
visual features, first 10 centroid distance-based Fourier descriptors [ZL01] are computed.
2) Geometric features: Depth information and Ray-based features (DR). To be more powerful in characterizing diverse typesof 3D models, two additional features D and R, developed by
Vranic [Vra04], are integrated together with Z and F such that weform the proposed hybrid shape descriptor ZFDR [LJ13]. D are thedepth buffer-based features of six standard depth views of a 3Dmodel, while R are the ray-based features of the 3D model which
are generated by shooting a number of rays from the center of themodel to its farthest surface intersection points.
Finally, a ZFDR hybrid shape descriptor distance is generated bylinearly combining the four component distances dZ , dF , dD , anddR , which are measured based on Scaled-L1 [Vra04] or Canberra
distance metric.
Without training, it is direct and efficient to extract our ZFDR
shape descriptor and to perform retrieval. The method is implemented in C/C++ and run on a Windows laptop machine with a 2.70
GHz Intel Core i7 CPU and 16.0 GB memory. The running time
information is as follows. The average time to calculate the ZFDR
shape descriptor of a 3D model is 2.67 seconds (CPCA: 1.45, ZF:
1.10, D: 0.03, R: 0.06). On average, it takes 34.2 microseconds
to compare a pair of 3D models, or 1.75 seconds to compare one
query model with all the 51162 target 3D models. Totally, it takes
4.42 seconds to perform a query, which includes feature extraction
for the query model and comparison with all the 51162 3D models
in the target dataset.

5.8. Deep VoxelNet, by S. Mk
We trained a 3D convolution network on 64x64x64 binary voxel
grids. The network was trained to classify the input grids into 203
categories given by the subsynset ids in the ShapeNet Core 55
dataset. An intermediate layer of this trained network is then used
as a feature vector to be able to perform 3D shape retrieval.

5.8.1. Pre-processing
The only processing applied is to convert the given OBJ files into
binary voxel grids. We used binvox [Min11] to perform this discretization.
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

Figure 7: Overview of VoxelNet. The network structure of the Deep
VoxelNet is summarized.

5.8.2. Network architecture and training
The network architecture is as illustrated in Figure1 . The networkis 3-wide and 7-deep, and every filter depth is 1 (that is, every filter has a 1-channel output), followed by ReLU activation. Aftereach filter, zero padding is done to maintain the output size sameas input size. The voxel grid is fed through 3 branches, where eachbranch has 3 layers. The outputs after the 3 layers and the input areconcatenated along the channel axis. We then apply Max Poolingof 2x2x2, thus halving the size. The pooled output is again sent
through three 3-layer branches, this time with smaller filter sizes.
The outputs at the sixth layer and the first max pool output are again
concatenated, and halved by max pooling. This output is then sent
to three 1-layer branches. Their outputs and the second max pool
output are concatenated and then flattened into a vector. This isthen fully connected to a 1000-neuron layer. The penultimate 1000neuron layer is again fully connected to a 203-neuron layer which
provides the classification outputs. We then apply softmax over the203 outputs, to provide a distribution over the classes.
The weights in the network are initialized by the Xavier uniform
initialization method and the entire network is trained from scratchusing Stochastic Gradient Descent with Nesterov momentum. The
network was trained for 6 iterations over the entire training set of
70000+ shapes (normal and perturbed). The classification loss used
was categorical cross-entropy.
For all of the above, we used Tensorflow running on two GTX1070 GPUs. We achieved a classification accuracy of 75.8% on
the normal training set, and 74.8% on the perturbed training set.
Validation accuracy obtained was 52.3% on normal, and 31% on
the perturbed set.
5.8.3. Retrieval
For every voxel grid, we use the 1000-dimensional vector output by
the penultimate layer of the network as the corresponding feature
vector. Retrieval was performed by building a KD-Tree on thesefeature vectors. For every shape, we query the nearest neighbors
in this 1000-dimensional feature space and output the retrieved results.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

DatasetMethod

P@N

R@N

micro
F1@N mAP

NDCG@N

P@N

R@N

macro
F1@N mAP

NormalKanezaki
Zhou
TatsumaFuruya∗Thermos
Deng
Li
Mk
SHREC16-Su
SHREC16-Bai

0.810
0.786
0.765
0.818
0.743
0.418
0.535
0.793
0.770
0.706

0.801
0.773
0.803
0.689
0.677
0.717
0.256
0.211
0.770
0.695

0.798
0.767
0.772
0.712
0.692
0.479
0.282
0.253
0.764
0.689

0.772
0.722
0.749
0.663
0.622
0.540
0.199
0.192
0.735
0.640

0.865
0.827
0.828
0.762
0.732
0.654
0.330
0.277
0.815
0.765

0.602
0.592
0.518
0.618
0.523
0.122
0.219
0.598
0.571
0.444

0.639
0.654
0.601
0.533
0.494
0.667
0.409
0.283
0.625
0.531

0.590
0.581
0.519
0.505
0.484
0.166
0.197
0.258
0.575
0.454

0.583
0.575
0.496
0.477
0.418
0.339
0.255
0.232
0.566
0.447

0.656
0.657
0.559
0.563
0.502
0.404
0.377
0.337
0.640
0.548

Perturbed

Furuya∗Tatsuma
Zhou
Kanezaki
Deng
Li
Mk
SHREC16-Bai
SHREC16-Su

0.814
0.705
0.660
0.655
0.412
0.496
0.690
0.678
0.632

0.683
0.769
0.650
0.652
0.706
0.234
0.012
0.667
0.613

0.706
0.719
0.643
0.636
0.472
0.258
0.020
0.661
0.612

0.656
0.696
0.567
0.606
0.524
0.172
0.009
0.607
0.535

0.754
0.783
0.701
0.702
0.642
0.303
0.043
0.735
0.653

0.607
0.424
0.443
0.372
0.120
0.199
0.546
0.414
0.405

0.539
0.563
0.508
0.393
0.659
0.373
0.052
0.496
0.484

0.503
0.434
0.437
0.333
0.164
0.179
0.052
0.423
0.416

0.476
0.418
0.406
0.327
0.329
0.215
0.047
0.412
0.367

0.560
0.479
0.513
0.407
0.395
0.336
0.109
0.518
0.459

NDCG@N

Table 2: Summary table of evaluation metrics for all participating teams and methods. Methods are ranked by the average of the micro andmacro mAP. * indicates the method is based on point set representation and indicates the method is based on volumetric representation.

6. Results
The summary results for all participating methods are given in Table 2. The corresponding precision-recall plots are given in Figure 8. We can draw several conclusions from the summary evaluation metrics.
First of all, it is exciting to observe that the top submissions thisyear outperform the best methods in SHREC16 significantly. Similar to last year’s competition, most of the submissions are basedon deep learning approaches. Shape retrieval is benefiting from therecent developments in deep learning methods for computer vision.
However, unlike last year when all approaches were projectionbased, the approaches this year are based on various input formats,including multiple projected views, point sets, 3D voxel grids andtraditional 3D shape descriptors. The point set–based approach of
Furuya has the best overall performance for the randomly oriented
3D model dataset. The projection-based approach of Kanezaki performs the best on the normal, consistently aligned 3D models.
The overall retrieval performance on the normal test dataset as
measured by micro-averaged mAP and NDCG values is fairly high(in the 70-80% range for mAP and in the high 80% to 90% range
for NDCG). The mAP measure is highly correlated with the AUC
giving a sense of the area under the precision-recall curve for eachmethod’s chosen retrieval list length. Similarly, NDCG evaluates
the overall relevance ranking of the retrieved model list against
an ideal ranking of the same retrieved list. All methods perform
well when judged on the chosen retrieval list lengths. However, themethods have very different trade offs of performance when we
consider precision and recall. For example, Mk have high P@Nvalues but quite low R@N values, whereas Deng have high R@Nvalues but lower P@N values. Other methods are more balanced inprecision and recall.
When using macro-averaging instead, we no longer adjust forclass sizes, thus giving proportionally more weight to synsets withfewer models. The same relative patterns between methods as observed in micro-averaging hold with macro-averaging as well, but
the overall performance for all methods is significantly lower, as
expected (F1@N scores drop from a high in the 70% range to ahigh in the 50% range).
When contrasting the normal dataset with the perturbed dataset
we see that, as expected, there is a drop in performance for all methods, though this drop is much less for Furuya which comes up as
the best performer for un-aligned 3D model data. This is an interesting strengh of the approach which is based on the local features
learned on an oriented point set of the 3D mesh.
Overall, deep learning models again achieve high performancein this competition compared to the more traditional approach by
Li. Projection-based inputs are used by most submissions, because
it is easy to make use of additional image data to help learn features for 3D shapes. Because of this, the models are based on image classification models and they are not built from scratch for the
3D problems. However, in the case of unknown rotations, the point
set–based approach of Furuya which uses local point features outperforms the view–based approaches. These results indicate that aneven more targeted exploration of the impact of different 3D shape
representations for deeply learned methods would be interesting.
Though projection-based models have dominated in computer vision, there is much space for improvement using volumetric, orpoint set–based methods.
c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55
DatasetmicroALL

macroALL

1.0
0.9
0.8
0.7
0.6
P

Normal0.5
MethodKanezaki RotationNet
Zhou ImprovedGIFT
Tatsuma ReVGG
Furuya DLAN
Thermos MVFusionNet
Deng CM-VGG5-6DB
Li ZFDRMk DeepVoxNet
SHREC16 Bai GIFT
SHREC16 Su MVCNN

0.4
0.3
0.2
0.1
1.0
0.9
0.8
0.7
0.6
P

Perturbed

0.5
0.4

MethodKanezaki RotationNet
Zhou ImprovedGIFT
Tatsuma ReVGG
Furuya DLAN
Thermos MVFusionNet
Deng CM-VGG5-6DB
Li ZFDRMk DeepVoxNet
SHREC16 Bai GIFT
SHREC16 Su MVCNN

0.3
0.2
0.1
0.0

0.1

0.2

0.3

0.4
R

0.5

0.6

0.7

0.8

0.0

0.1

0.2

0.3

0.4
R

0.5

0.6

0.7

0.8

Figure 8: Precision-recall plots for all participating teams and methods, on the normal and perturbed datasets.

7. Conclusion
In this paper, we compared several methods for 3D model retrieval
on a large-scale dataset of 3D models. Most methods use someform of neural network architecture, and outperform a more traditional non-learning based approach based on 3D shape descriptors.
Though the performance on a consistently aligned version of thedataset is fairly high, there is still much space for improvement,
particularly on the more challenging case of 3D model data withunknown orientations.
Compared with last year’s iteration of this competition where
all teams leveraged view-based methods, this year we observe ahigher diversity in the input 3D representations, including point
set–based and volumetric methods. The point set method of Furuya and Ohbuchi performs the best in the unaligned 3D model test
scenario. In contrast the view-based method of Kanezaki performs
the best on the consistently aligned 3D model data. An interestingc 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.

direction for future work is to consider integrating the discriminative power of view-based approaches and the robustness to arbitrary orientation exhibited by approaches reasoning more locallywith geometry.

Acknowledgments: Atsushi Tatsuma and Masaki Aono were supported by Kayamori Foundation of Informational Science Advancement, Toukai Foundation for Technology, and JSPS KAKENHI Grant Numbers 26280038, 15K15992. Bo Li is partially supported by the University of Southern Mississippi Faculty Startup
Funds Award. The research done by Henry Johan is supported by
the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding
Initiative.

M. Savva, F. Yu, H. Su et al. / SHREC’17 Track: Large-Scale 3D Shape Retrieval from ShapeNet Core55

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c 2017 The Author(s)Eurographics Proceedings c 2017 The Eurographics Association.