Blind Projection-based 3D Point Cloud Quality Assessment Method

using a Convolutional Neural Network

Salima Bourbia

1 a

, Ayoub Karine

2 b

, Aladine Chetouani

3 c

and Mohammed El Hassouni

1,4 d

LRIT, Mohammed V University in Rabat, Rabat, Morocco

L@bISEN, ISEN Yncr

ea Ouest, 33 Quater Chemin du Champ de Manœuvre, 44470 Carquefou, France

Laboratoire PRISME, Universit

e d’Orl

eans, France

FLSH, Mohammed V University in Rabat, Rabat, Morocco

Keywords:

Point Cloud, Quality Assessment, Multiple View Projection, Convolutional Neural Network (CNN).

Abstract:

In recent years, 3D point clouds have experienced rapid growth in various ﬁelds of computer vision, increasing

the demand for efﬁcient approaches to automatically assess the quality of 3D point clouds. In this paper, we

propose a blind point cloud quality assessment method based on deep learning, that takes an input point cloud

object and predicts its quality score. The proposed approach starts with projecting each 3D point cloud into

rendering views (2D images). It then feeds these views to a deep convolutional neural network (CNN) to

obtain the perceptual quality scores. In order to predict accurately the quality score, we use transfer learning

to exploit the high potential of VGG-16, which is a classiﬁcation model trained on ImageNet database. We

evaluate the performance of our model on two benchmark databases: ICIP2020 and SJTU. Based on the

results analysis, our model shows a strong correlation between the predicted and the subjective quality scores,

showing promising results, and outperforming the state-of-the-art point cloud quality assessment models.

1 INTRODUCTION

With the rapid improvement of 3D acquisition and

rendering devices, 3D point clouds (PCs) have re-

ceived more and more attention and are used in vari-

ous 3D applications such as autonomous driving, 3D

games, cultural heritage, robotics, etc. A point cloud

is a collection of a large amount of points that allow

representing 3D scenes and objects. Each point is

characterized by geometric coordinates (x, y, z) and

associated attributes such as color, normal vectors,

curvature, reﬂectance, etc. During the processing

chain (acquisition, compression, transmission, ren-

dering), the PC can be affected by several distortions

that deteriorate their visual quality. Therefore, the

development of methods that measure these distor-

tions is of paramount importance. This research ﬁeld

is called Point Clouds Quality Assessment (PCQA).

This task can be achieved using subjective quality as-

https://orcid.org/0000-0001-5664-7514

https://orcid.org/0000-0002-9304-4613

https://orcid.org/0000-0002-2066-4707

https://orcid.org/0000-0002-6741-4799

sessment methods, which are considered the most ro-

bust and accurate way to assess the perceptual quality.

However, these metrics are based on human opinions,

making them time-consuming, expensive and imprac-

tical in real-world applications. Therefore, another

type of metrics has emerged, so-called objective qual-

ity assessment methods based on computational mod-

els that automatically predict the perceptual quality

score. Consequently, the most PCQA methods are ob-

jective and are classiﬁed into three categories: Full-

Reference (FR), Reduced-Reference (RR) and No-

Reference (NR). The FR-PCQA metrics require the

presence of reference point clouds. In the RR-PCQA,

only a portion of the original point cloud information

(features) is available. Finally, the NR-PCQA met-

rics do not require the reference information to mea-

sure the visual quality, which make them suitable for

several multimedia applications. Generally, the NR

methods for multimedia content are done through ma-

chine/deep learning methods (Bourbia et al., 2021;

Chetouani, 2018; Chetouani, 2014). For instance,

the convolutional neural networks (CNN) have dom-

inated the no-reference image quality assessment.

However, contrary to the 2D images (regular grid),

518

Bourbia, S., Karine, A., Chetouani, A. and El Hassouni, M.

Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network.

DOI: 10.5220/0010872700003124

In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages

518-525

ISBN: 978-989-758-555-5; ISSN: 2184-4321

the PC are unordered and without spatial structure.

Thus, the CNN cannot be used directly on PC. To

omit this limit, we project the PC into multiple 2D

images (views). Then, we feed this latter to a CNN

model in order to achieve the quality score of the PC

by averaging the quality assessment of each view.

The structure of this paper is organized as follows.

Section 2 reviews related work on Point Cloud Qual-

ity Assessment (PCQA). In Section 3, we describe the

proposed NR-PCQA. Then, Section 4 is dedicated to

the experiments. Finally, we draw conclusion and per-

spectives in Section 5.

2 RELATED WORK

To address the problem of Point Cloud Quality As-

sessment (PCQA), many approaches have been pro-

posed in the literature, which can broadly classiﬁed

in two categories: point-based and projection-based

metrics.

In point-based methods, the ﬁrst proposed meth-

ods are Point-to-point metrics that compute the geo-

metric distance between the corresponding points of

the reference and the distorted point cloud. How-

ever, these methods do not consider the surface struc-

tures representation. The point-to-plane metrics mea-

sure the geometric distortion by projecting the point-

to-point distance vector along the normal direction.

Other point-based methods have been proposed in

light of these researches. Alexiou et al. (Alexiou and

Ebrahimi, 2018) proposed a metric based on measur-

ing the angular similarity between the tangent planes

of a point from reference and its degraded version.

Javaheri et al.(Javaheri et al., 2020b) compute the ge-

ometric distance between a point and a distribution

of points from the other point cloud based on the

well-known Mahalanobis distance. This work is ex-

tended to evaluate the color distortion by fusing the

geometry and the color point-to-distribution distor-

tions to obtain the joint geometry and color quality

metric (Javaheri et al., 2021). Meynet et al. (Meynet

et al., 2019) present PC-MSDM metric inspired by

the well-known Structural Similarity (SSIM) for 2D

images (Wang et al., 2004). Taken a point cloud of

reference and its distorted version, the authors estab-

lish the difference structure of local curvature statis-

tics to produce a quality score that indicates the distor-

tion level in PC. The same authors proposed another

work, so-called Point Cloud Quality Metric (PCQM)

(Meynet et al., 2020). They select various geometry

and color based features and combine them through

a linear model. Viola et al. (Viola et al., 2020)

proposed a geometry and color based metric by em-

ploying color statistics including histogram and cor-

relogram to capture color impairment, and using a

point-to-point method to compute the geometric dis-

tortions. Finally, a linear combination is used to ob-

tain an overall quality score. Alexiou et al. (Alexiou

and Ebrahimi, 2020) extract several features to cap-

ture the existing local changes between the reference

and the distorted PC and predict their quality. Finally,

Diniz et al.(Diniz et al., 2021) use color and geom-

etry descriptors to extract statistic features from the

reference and the distorted PC in order to estimate the

quality score.

In projection-based methods, the authors project

the 3D point clouds into different 2D views, and then

they evaluate the quality of the point clouds. Torlig

et al. (Torlig et al., 2018) project the voxelized 3D

PC into six 2D orthographic projections and each

reference and its corresponding distorted version are

evaluated based on 2D objective metrics, including

Peak Signal-to-Noise Ratio (PSNR), Visual Informa-

tion Fidelity in Pixel domain (VIFP) and Multi-Scale

Structural Similarity (MS-SSIM). Then, they average

all the obtained scores into one total score that indi-

cate the level of degradation in the PC. Yang et al.

(Yang et al., 2020) aggregate the global and the local

features extracted from all the six perpendicular color

texture and depth images projected from the reference

and the distorted PC, in order to have the ﬁnal objec-

tive quality. Chen et al. (Chen et al., 2021) proposed a

hybrid method that extract from the layered reference

PCs and their distorted versions, geometric features

based on projection-based method and color features

based on the point-based method. Then, the distortion

values of PCs are computed through a weighted linear

combination of all the extracted features.

We remark that most of the state-of-the-art meth-

ods of PCQA are FR that count on the presence of the

reference PC for the assessment, while the NR meth-

ods are less studied in the literature, due to the high

complexity of their task. In this context, and based

on the large demand of NR methods in several real

life multimedia applications, we propose a novel no-

reference PCQA method that predicts the perceptual

quality of point clouds without access to the refer-

ence.

3 PROPOSED METHOD

The ﬂowchart of our proposed method is presented in

Figure 1. First, we render the 3D point clouds from

multiple points of view by projecting the point clouds

into 2D images. Then, we split the images into central

patches in the vertical direction before feeding them

Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network

519

Figure 1: Flowchart of the proposed NR-PCQA method.

to a CNN that predicts the scores of each patch. Fi-

nally, the quality score of each 3D point cloud is ob-

tained by averaging the scores of the patches.

3.1 Point Cloud Rendering

The ﬁrst step consists of rendering 2D projections

from 3D point clouds at multiple viewpoints. Since

the information in the point cloud is different accord-

ing to the point of view from which it is rendered, we

ﬁxed virtual cameras at different angles to surround

the 3D point clouds. For each point cloud, the cen-

troid of PC is positioned at the origin of the spherical

coordinate system (Abouelaziz et al., 2018; Aboue-

laziz et al., 2020a; Abouelaziz et al., 2020b). The

coordinates of the virtual cameras (r, θ

, φ

) are ob-

tained by varying the angle φ

∈ [0, 2π] by

and set-

ting θ

to zero. The distance (r) between the camera

and the 3D object is varying according to the size of

the object. After that, we sample the projected images

into 4 central overlapping patches of size 224 × 224

pixels in vertical direction with stride 90 in order to

discard the useless backgrounds. Then, we normalize

each image patch by a local normalization, as demon-

strated in equation 1. The size of each projected view

is 512 × 512 pixels, then we obtain 12 × 4 = 48 nor-

malized patches from each PC.

I(i, j) =

I(i, j) − µ(i, j)

σ(i, j) +C

(1)

µ(i, j) =

(2P + 1) × (2Q + 1))

p=P

∑

p=−P

q=Q

∑

q=−Q

I(i+ p, j +q)

σ(i, j) =

p=P

∑

p=−P

q=Q

∑

q=−Q

(I(i + p, j + q) − µ(i, j))

where

I(i, j) is the normalized intensity value of the

I(i, j) pixel at the (i, j) location, µ(i, j) and σ(i, j) in-

dicate the local mean and variance, respectively. C

is a constant value that sets at 1 to prevent division

by zero, P and Q are the normalization window sizes

(Kang et al., 2014).

Figure 2: The adjusted architecture of VGG-16 for 3D point

cloud quality evaluation.

3.2 Convolutional Neural Network

Motivated by the high performance of the VGG-16

architecture (Simonyan and Zisserman, 2014) in dif-

ferent computer vision ﬁelds (Chetouani, 2019; Gao

et al., 2017; Zhang et al., 2020; Karine et al., 2020;

Fourati et al., 2020), we exploit this model to esti-

mate the quality score of the PC patches. More pre-

cisely, we adopt a transfer learning approach by trans-

ferring the hyperparameters of the pre-trained VGG-

16 on ImageNet dataset (Deng et al., 2009). VGG-16

model is composed of 13 convolutional layers, 5 max-

pooling layers and 3 fully-connected layers. This net-

work is characterized by utilizing small kernels size

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

520

Figure 3: Reference point cloud of (a) ICIP2020 and (b) SJTU databases.

that enables reducing the computational complexity

of the model while improving its generalization abil-

ity. We adjust the original model architecture to make

it suitable for the NR-PCQA task by modifying the

size of the last three fully-connected layers in order

to adapt it to regression, as shown in Figure 2. Each

fully-connected layer is followed by a dropout set to

0.5 and a Rectiﬁed Linear Unit (ReLu) except the out-

put layer that has a Sigmoid function to predict the

normalized quality score patches. The ﬁnal quality

score is computed by averaging the quality scores of

all extracted PC patches.

3.3 Training

We underline that the normalized Mean Opinion

Scores (MOS) of each PC is affected to all associated

patches. To quantify the error between the predicted

) and the subjective (MOS

) quality score, we use

the L

loss function, deﬁned as follows :

L oss =

∑

n=1

− MOS

| (2)

where N is the batch size set to 128. To optimize

the model parameters, we use the Stochastic Gradi-

ent Descent (SGD) with a momentum set to 0.9 and

a weight decay equal to 0.0001, the learning rate is

initialized with 10

−3

and is reduced at each 70 epochs

with gamma equal to 0.1. Pytorch framework (Paszke

et al., 2019) is used for the implementation.

4 EXPERIMENTAL RESULTS

In this section, we ﬁrst outline the point cloud

databases and the protocol used to train and test the

proposed model. Then, we deﬁne the performance

evaluation criteria. Finally, we compare and discuss

the performance results of our model with the state-

of-the-art methods.

4.1 Point Cloud Databases

To evaluate the performance of our model, we used

two benchmark subjective databases:

• ICIP2020 (Perry et al., 2020): contains 6 ref-

erence PCs, as illustrated in ﬁgure 3 (a) (4 PCs

are with complete coverage and 2 point clouds are

with semi-coverage: Sarah9 and Ricardo10), and

Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network

521

Table 1: Performance comparison of the proposed method against the state-of-the-art methods on ICIP2020 database.

Type Type of methods Methods SROCC PLCC

Full-reference methods (FR) Point-based methods D1 MSE (Tian et al., 2017) 0.954 0.615

D1 Hausdorff (Javaheri et al., 2020a) 0.682 0.615

D2 MSE (Tian et al., 2017) 0.971 0.618

D2 Hausdorff (Javaheri et al., 2020a) 0.735 0.491

MMD P2D-G (Geometry) (Javaheri et al., 2020b) 0.960 0.784

MMD P2D- JGY (Joint Geometry and Color) (Javaheri et al., 2021) 0.965 0.881

Feature-based methods PCQM method (Meynet et al., 2020) 0.977 0.942

PointSSIM (Alexiou and Ebrahimi, 2020) 0.795 0.717

Angular-based method Angle-MSE (Alexiou and Ebrahimi, 2018) 0.902 0.626

No-reference methods (NR) Deep learning-based method The proposed NR-PCQA 0.981 0.951

Table 2: Performance comparison of the proposed method against the state-of-the-art methods on SJTU database.

Type Type of methods Methods SROCC PLCC

Full-reference methods (FR) Point-based methods D1 MSE (Tian et al., 2017) 0.803 0.606

D1 Hausdorff (Javaheri et al., 2020a) 0.687 0.606

D2 MSE (Tian et al., 2017) 0.715 0.568

D 2 Hausdorff (Javaheri et al., 2020a) 0.683 0.562

MMD P2D-G (Geometry) (Javaheri et al., 2020b) 0.604 0.628

MMD P2D- JGY (Joint Geometry and Color) (Javaheri et al., 2021) 0.755 0.667

Feature-based methods PCQM method (Meynet et al., 2020) 0.807 0.805

PointSSIM (Alexiou and Ebrahimi, 2020) 0.685 0.652

Angular-based method Angle-MSE (Alexiou and Ebrahimi, 2018) 0.772 0.615

No-reference methods (NR) Deep learning-based method The proposed NR-PCQA 0.927 0.921

90 distorted versions achieved by 3 compression

methods: G-PCC Octree, G-PCC Trisoup and V-

PCC with 5 different levels from the low to the

high quality.

• SJTU (Yang et al., 2020): contains 9 refer-

ence point clouds, as shown in ﬁgure 3 (b) (all

the objects are with complete coverage) and their

378 distorted versions degraded using 7 types

of distortions that can appear on point clouds

in practical applications: Octree-based compres-

sion, Color Noise, Down-scaling, Down-scaling

and Color noise, Geometry Gaussian noise, Color

noise and Geometry Gaussian noise.

To make a fair comparison, we conduct the same

experimental protocol to our method and the state-of-

the-art baselines. Especially, we use a k-fold cross

validation. One fold (object) is used for the test phase

and one for the validation phase, while the remain-

ing objects (k-2 folds) are used for the training phase,

with no-overlapping between the training, validation

and test sets. We mention that the 2 semi-coverage

PCs of ICIP2020 database are not taken into account

in this experiment. Consequently, the number of folds

in ICIP2020 equals to 4 and equals to 7 for SJTU.

4.2 Evaluation Metrics

To evaluate the performance of our model against the

other quality metrics, we adopt two evaluation criteria

(Zhai and Min, 2020):

1. Spearman Rank Order Coefﬁcient (SROCC):

measures the monotonicity between the predicted

and the subjective quality score as follows :

SROCC = 1 −

∑

n(n

− 1)

(3)

where d

represents the rank distance between pre-

dicted and subjective quality score (MOS), and n

is the number of the used point clouds.

2. Pearson Linear Correlation Coefﬁcient (PLCC):

computes the strong correlation between the pre-

dicted and the ground truth score using the follow-

ing equation :

PLCC =

∑

i=1

− M

)(M

− M

)

∑

i=1

− M

)(M

− M

)

(4)

where M and M

represents the ground truth and

the predicted quality score, respectively. The i

represents the number of instance on the test set.

The absolute values of both criteria vary between 0

and 1, a higher value indicates the best quality predic-

tion performance. The correlation result is the mean

of the computed correlations in each cross-validation

iteration.

VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications

522

4.3 Performance Comparison

In order to evaluate the proposed method, we make

a comparison with different state-of-the-art metrics

on ICIP2020 and SJTU databases, we split the

state-of-the-art metrics into 4 groups: Point-based,

Feature-based, Angular-based, and Deep learning-

based methods. The highest values of SROCC and

PLCC are highlighted in bold in order to ﬁnd the best

correlation values. The results on ICIP2020 are re-

ported in Table 2. As we can observe, our model

achieves the highest correlation compared to point-

based methods in a range varying between 1% and 3%

for SROCC, and between 7% and 46% for PLCC. In

the features-based methods, the proposed method out-

performs the PointSSIM metric with 19% in SROCC

and 24% in PLCC, and shows a competitive perfor-

mance in SROCC compared to PCQM metric while

surpassing it in PLCC with 1%. Finally, compared to

the angle-based method, our method achieves a higher

performance in SROCC and PLCC metrics.

We show in Table 2 the comparison results on

SJTU database. The proposed method presents the

best performance overall the nine FR-PCQA state-of-

the-art metrics. Our metric presents a gain varying

between 12% and 32% in SROCC, 25% and 36%

in PLCC for the point-based methods, and exceeds

the features based methods with a variation between

12% and 24% in SROCC and between 27% and 12%

in PLCC. In both PLCC and SROCC, the proposed

model performs better than angular-based method,

more precisely, it is higher with 15% for SROCC and

31% for PLCC.

We highlight that the methods incorporating ge-

ometry and color distortion information achieved

good performance compared to the methods based

only on the simple geometric distance. Further-

more, the performance of all models on the ICIP2020

database is better than the SJTU database, as demon-

strated in ﬁgure 4. That can be explained by the

type of distortion in both databases, in ICIP2020 there

are only compression types while in SJTU there is

more challenging distortion types including acquisi-

tion noise. However, our model was able to achieve

the best performance on both databases and outper-

form the state-of-the-art methods.

We evaluate the ability of our model to predict

the perceptual quality score by comparing the scores

predicted by our model and their corresponding sub-

jective scores for 3 different distortion types (G-PCC

Trisoup R02, GPCC Octree R01, and VPCC RO5)

applied on the RedandBlack object in the ICIP2020

database. The object with the highest quality is scored

by 5 and the object with the lowest quality is scored by

Figure 4: PLCC of comparison metrics on ICIP2020 and

SJTU databases.

1. As we can observe in Figure 5, our model was able

to effectively predict the quality scores, which proves

the robustness of the proposed model. Based on the

obtained results, we emphasize that deep learning-

based methods represent an outstanding advantage in

learning sophisticated features and predicting the per-

petual quality with high accuracy compared to the

handcrafted methods.

5 CONCLUSION

In this paper, we proposed a deep learning no-

reference point cloud quality assessment method that

projects a 3D point cloud into multi-viewpoint and

uses them as an input to a deep convolutional neural

network. We relied on the concept of transfer-leaning

in order to help the CNN model to ﬁnd the best map-

ping between the input 2D projected views and the

quality scores. It is noteworthy that our model is a

no-reference method that does not require any refer-

ence data and allows the point cloud quality evalua-

tion in a single optimization process, which represents

a promising track in practical situations. We demon-

strated through the obtained experimental results that

our model shows a better performance in predicting

quality scores compared to the full-reference state-

of-the art methods. As future work, we aim to in-

clude the semi-coverage objects case by completing

the 3D shape of the PCs. Also, we plane to test

the performance of the proposed method with dif-

ferent pretrained models (Resnet(He et al., 2015),

Alexnet(Krizhevsky et al., 2012), etc.).

Blind Projection-based 3D Point Cloud Quality Assessment Method using a Convolutional Neural Network

523

Figure 5: Perceptual quality score comparison between the proposed model and the subjective quality score (MOS) on

RedandBlack object from ICIP2020 dataset.

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