next viewpoint recommendation by pose ambiguity ...ide/res/paper/e18-conference-has… · viewpoint...

Next Viewpoint Recommendation by Pose Ambiguity Minimizationfor Accurate Object Pose Estimation

Nik Mohd Zarifie Hashim1,5, Yasutomo Kawanishi1, Daisuke Deguchi2, Ichiro Ide1, Hiroshi Murase1,Ayako Amma3 and Norimasa Kobori4

1Graduate School of Informatics, Nagoya University, Japan2Information Strategy Office, Nagoya University, Japan

3Toyota Motor Corporation, Japan4Toyota Motor Europe, Belgium

5Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, [email protected], [email protected], [email protected], [email protected],

[email protected], ayako [email protected], [email protected]

Keywords: 3D Object, Deep Learning, Next Viewpoint, Pose Ambiguity, Pose Estimation.

Abstract: 3D object pose estimation by using a depth sensor is one of the important tasks in activities by robots. Toreduce the pose ambiguity of an estimated object pose, several methods for multiple viewpoint pose estimationhave been proposed. However, these methods need to select the viewpoints carefully to obtain better results. Ifthe pose of the target object is ambiguous from the current observation, we could not decide where we shouldmove the sensor to set as the next viewpoint. In this paper, we propose a best next viewpoint recommendationmethod by minimizing the pose ambiguity of the object by making use of the current pose estimation result asa latent variable. We evaluated viewpoints recommended by the proposed method and confirmed that it helpsus to gain better pose estimation results than several comparative methods on a synthetic dataset.

1 INTRODUCTION

3D object pose estimation which estimates the threeaxes rotation of a target object, has recently becomeone of the focussed topics in the machine vision fieldfor application on tasks in activities by robots. Espe-cially, object picking is an essential task for industrialrobots and home helper robots. For example, homehelper robots are required to pick an object and passit to a person.

As a machine vision problem, robots require asensor for observing their surrounding environment.There are several types of sensors utilized to observethe environment, e.g. light detection and ranging (Li-DAR), stereo cameras, RGB and depth (RGB-D) ca-meras, and so on. All these sensors are being activelyimproved year by year. In this paper, since they arerobust to the object’s texture and can obtain much in-formation about the object’s shape, we focus on depthimages captured by a depth sensor, and use them forestimating the object’s pose.

Among techniques for pose estimation from depthimages, the simplest approach is estimating an ob-

ject’s pose from a single depth image captured from acertain viewpoint. In this approach, the object’s poseis described as a relative pose to the sensor.

If an object has a distinct shape to be distinguis-hed from various viewpoints, object pose estimationwould be easy. However, most objects have view-points where their poses cannot be uniquely distin-guished by their appearances because they resembleeach other. We call this the “pose ambiguity pro-blem”. In single viewpoint pose estimation, this pro-blem leads to inaccurate pose estimation.

Since a robot can move and thus change view-points, after the initial observation, it can move toanother viewpoint and re-observe the object. By ob-serving an object from multiple viewpoints, the am-biguity could be reduced. In this approach, to esti-mate the object’s pose accurately, it is necessary tochoose the best next viewpoint. A better viewpointhelps us to achieve a more accurate object pose esti-mation as shown in Figure 1. However, if the poseestimation result from the initial viewpoint is ambi-guous, the robot could not properly decide in whichdirection and how far it should move to reach the best

60Hashim, N., Kawanishi, Y., Deguchi, D., Ide, I., Murase, H., Amma, A. and Kobori, N.Next Viewpoint Recommendation by Pose Ambiguity Minimization for Accurate Object Pose Estimation.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP, pages60-67ISBN: 978-989-758-354-4Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

The best

next viewpoint

Low ambiguity

Initial

viewpoint

move

move?

?

Figure 1: Recommendation of the best next viewpoint.

next viewpoint. The question here is that, how can weknow the best next viewpoint from the current obser-vation as illustrated in Figure 2.

Research on pose estimation from multiple vie-wpoints is actively proposed by the instance-levelapproach (Doumanoglou et al., 2016) (Sock et al.,2017). Though this approach seems to work, if anapplication needs to estimate a pose between variousobject shapes in a same category, they may not per-form well. Thus, we focus on the category-level poseestimation.

In this paper, we propose a method for the nextviewpoint recommendation for accurate object poseestimation even if there are various shapes in the samecategory. To evaluate the effectiveness of the recom-mended viewpoint, we define a metric called “poseambiguity”, which reflects how ambiguous the poseestimation is. By minimizing the pose ambiguity, wewill find the next viewpoint which will be the bestto estimate the object’s pose. This pose estimationis realized by averaging the pose estimation of twoviewpoints which are the initial observation and thenext viewpoint. To handle the pose estimation am-biguity of the initial observation, we make use of theestimated pose from the initial observation as a latentvariable. We introduce an estimation method of thepose ambiguity by marginalizing the latent variable,which can consider all possibilities of the initial esti-mation result. To make the problem simple and focuson the key idea, in this paper, we limit the movementof the sensor only to the z-axis rotation and consi-der the non-cascade case for analyzing the next view-point. However, the proposed method and discussioncould be straightforwardly extended to 3D rotation.We evaluate the effectiveness of the proposed methodon a dataset generated from a publicly available 3Dobject dataset: ShapeNet (Chang et al., 2015).

?

80° 90° 100°

High ambiguity0°

Low

ambiguity

Figure 2: Selection problem in pose estimation from multi-ple viewpoints.

Our contribution can be summarized as follows:

• We define a metric “pose ambiguity” to evaluatethe ambiguousness of the pose estimation.

• We propose a next viewpoint recommendationmethod which finds the best next viewpoint wherethe pose ambiguity is minimized.

• We show that the proposed method ourperformstwo other naive viewpoint recommendation met-hods, and also that it achieves a better result thanthe pose estimation result from a single viewpoint.

• We introduce a new paradigm of searching thebest next viewpoint for category-level object poseestimation compared to conventional instance-level object pose estimation.

The remaining of this paper is structured as fol-lows: In Section 2, related works that have been pre-viously proposed will be introduced. After that, wewill explain the proposed method in detail. Section 4will discuss the evaluation results. Finally, we con-clude our paper in Section 5.

2 RELATED WORK

In the past few years, many researchers proposed met-hods to tackle various difficulties in 3D object poseestimation. Here, we categorize the pose estimationmethods into the following two approaches: thosefrom a single viewpoint and those from multiple vie-wpoints.

Next Viewpoint Recommendation by Pose Ambiguity Minimization for Accurate Object Pose Estimation

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2.1 Object Pose Estimation from aSingle Viewpoint

The template matching approach is one of the ear-liest pose estimate method from a single viewpoint(Chin and Dyer, 1986). This method utilizes manytemplates of the target object captured from variousviewpoints beforehand, and the pose estimation resultis taken from the best matched template. To reducethe number of templates, Murase and Nayer (Mu-rase and Nayar, 1995) proposed the Parametric Ei-genspace method. This method represents an object’spose variation on a manifold in a low-dimensionalsubspace obtained by Principal Component Analysis(PCA). By interpolating the pose (template) of thetarget object on the manifold, we can achieve accu-rate object pose estimation even with few templates.Since PCA focuses on the appearance variation oftemplates, some poses with similar appearances maybe mapped to similar points in the low-dimensionalsubspace, which is difficult to distinguish. This dimi-nishes the accuracy of the pose estimation. Moreover,the method is based on PCA, which is an unsupervi-sed learning method, so it does not fully utilize thepose information for estimating the object’s poses.

Recently, Ninomiya et al. (Ninomiya et al., 2017)proposed a supervised feature extraction method forembedding templates into a pose manifold. Theyfocused on Deep Convolutional Neural Networks(DCNNs) (Krizhevsky et al., 2012) which is oneof the deep-learning models, as a supervised lear-ning method for manifold embedding. They modi-fied DCNNs for object pose estimation, named Pose-CyclicR-Net, which can correctly handle object rota-tion by describing the rotation angle using trigonome-tric functions. By introducing the Pose-CyclicR-Netbased manifold embedding, which is called Deep Ma-nifold Embedding, the method estimates the object’spose from a single viewpoint.

In general, object pose estimation from a singleviewpoint faces the problem of inaccurate pose esti-mation due to the ambiguity issue, namely, an objectmay have some poses which look similar and hard tobe distinguished.

2.2 Object Pose Estimation fromMultiple Viewpoints

To avoid the pose ambiguity issue, several methodsfocus on object pose estimation from multiple vie-wpoints. Collet and Srinivasa (Collet and Srini-vasa, 2010) proposed a multi-view object pose es-timation method based on multi-step optimizationand global refinement. Erkent et al. (Erkent et al.,

2016) tackle object pose estimation in cluttered sce-nes. This is a multi-view approach based on proba-bilistic, appearance-based pose estimation. Vikstenet al. (Viksten et al., 2006) proposed a method com-bining several pose estimation algorithms and infor-mation from several viewpoints. Zeng et al. (Zenget al., 2017b) proposed a self-supervised approachfor object pose estimation in the Amazon PickingChallenge (Zeng et al., 2017a) scenario. Kanezaki etal. (Kanezaki et al., 2018) proposed the RotationNet,which takes multi-view images of an object as inputand jointly estimates its pose and object category. Assuch, there are various methods for object pose esti-mation from multiple viewpoints, but these methodsdo not consider which viewpoint is effective for theestimation. Unlike others, in this paper we proposean idea of estimating the current viewpoint which willincrease the pose estimation later.

Recently, some researches focus on predictingNext-Best-View for object pose estimation. Douma-noglou et al. (Doumanoglou et al., 2016) and Sock etal. (Sock et al., 2017) proposed next-best-view pre-diction methods for multiple object pose estimationbased on Hough Forest (Gall and Lempitsky, 2009).We expect that this approach will be the next inte-resting topic. This idea will allow us to support anapplication in which various instances in a specificobject category need to be considered as the targetobject. However, we acknowledge that these methodscould not be applied for the category-level object poseestimation since they are designed only for instance-level object pose estimation. As the pose estimationon category-level has not been studied in the past, weinitiated the study with our proposed method.

3 NEXT VIEWPOINTRECOMMENDATION

3.1 Overview

In this paper, we propose a novel next viewpointrecommendation method based on pose ambiguityminimization; We define a metric called “pose am-biguity” given two different viewpoints which shouldbe minimized. Since the initial viewpoint may be am-biguous, by handling the current viewpoint as a la-tent variable, the pose ambiguity function is decom-posed into “pose ambiguity under given two view-points” and “viewpoint ambiguity under a given ob-servation”. Figure 3 illustrates the angle distributionfor the “pose ambiguity” G. Here, the minimum valueof G at y-axis infers the best next viewpoint as δ [◦]

VISAPP 2019 - 14th International Conference on Computer Vision Theory and Applications

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Figure 3: Pose ambiguity minimization (Input image with90◦ rotation angle).

from x-axis. This δ will be utilized to estimate all theobject poses. We calculate the pose estimation for twoviewpoints by averaging them as it provides us a re-liable pose between the two different angles. We willintroduce the details of the process in the followingsections.

3.2 Pose Ambiguity MinimizationFramework

This framework will measure the pose ambiguity in aquantitative way. First of all, what is pose ambiguity?Here, we define it as the difficulty to estimate the poseof an object from a viewpoint. Given a pose likeli-hood distribution over the possible poses, if the dis-tribution is largely diverse, the pose will be difficultto be estimated correctly. Thus, we define the poseambiguity G as a function of the pose likelihood dis-tribution p(θ). For example, G can be defined by theEntropy of p(θ) as

G(p) =∫−p(θ) log p(θ)dθ . (1)

Here, we evaluate the pose likelihood distributionunder an image observed from the initial viewpoint,and then yield the rotation angle to the best next vie-wpoint. Therefore, we define the pose likelihood dis-tribution as a conditional distribution p(θ |I,δ ) whenan image I from the current viewpoint and a rotationangle δ are given.

The minimum value of the pose ambiguity in Gwill tell us the best next viewpoint for accurate poseestimation using the two viewpoints. By using theformulation, we find the best viewpoint by minimi-zing the entropy as

δ = argminδ

G(p(θ |I,δ )). (2)

To handle the ambiguity of the initial viewpoint,we further decompose the pose likelihood distributionas follows:

p(θ |I,δ ) =∫

p(θ |φ ,δ )p(φ |I)dφ . (3)

The first term p(θ |φ ,δ ) indicates the pose likelihooddistribution under two given viewpoints φ and φ +δ ,and the rest part p(φ |I) indicates the viewpoint like-lihood under a given observation. In the followingsections, we explain more details on the two distribu-tions.

3.3 Estimation of Viewpoint LikelihoodDistribution p(φ |I)

Since the absolute viewpoint of an observation is dif-ficult to obtain, the viewpoint likelihood distributioncan be considered as a relative pose estimation fromthe initial viewpoint. In the ideal case, if we have adiscrete pose classifier in hand for the pose estima-tion, we may obtain not only the estimation result(pose) but also the likelihood for all possible poses.On the other hand, if we take a regression-based ap-proach for the pose estimation, such as Pose-CyclicR-Net proposed by Ninomiya et al. (Ninomiya et al.,2017), we may only obtain an estimation result suchas

φ = f (I), (4)

where I represents a given image and f the pose es-timator. For such a regression-based pose estimator,how can we obtain the viewpoint likelihood distribu-tion? Since we have many images Ii of various objectsin a class, by applying pose estimation for many ima-ges, we can obtain many pose estimation results φi.From these pose estimation results and their groundtruth, we can obtain a huge number of pairs of an esti-mation result and a ground truth. By applying densityestimation to the data, we can obtain a conditional dis-tribution as p(φ | f (i))= p(φgt|φest), where φgt repre-sents the ground truth and φest the estimation result.

By using the conditional distribution, we canobtain the viewpoint likelihood distribution as,

p(φ |I) = p(φ | f (I)) (5)

for a regression-based object pose estimator. This vie-wpoint likelihood distribution is illustrated in Figure4.


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Figure 4: Viewpoint likelihood distribution p(φ |I) (Inputimage with 90◦ rotation angle).

Figure 5: Pose likelihood distribution given two viewpointsp(θ |I,δ ) (Input image with 90◦ rotation angle).

3.4 Estimation of Pose LikelihoodDistribution p(θ |φ ,δ )

Here we explain the pose likelihood distribution giventwo viewpoints φ and φ + δ , where φ represents thecurrent viewpoint and δ the rotation angle to the nextviewpoint. The likelihood represents how accuratelythe objects’ pose can be estimated given the two vie-wpoints. The pose likelihood distribution given twoviewpoints is illustrated in Figure 5. Here, we simplydecompose the likelihood distribution into two poselikelihoods as

p(θ |φ ,δ ) = p(θ |φ)p(θ |φ +δ ), (6)

where p(θ |φ) and p(θ |φ + δ ) denote the pose like-lihood distributions given a viewpoint φ and φ + δ ,respectively. This equation holds by assuming p(θ),which is the pose likelihood without any information,follows a uniform distribution. Each likelihood dis-

Figure 6: Example images from the “Mug” class in theShapeNet dataset (Chang et al., 2015).

tribution given a viewpoint can also be calculated byapplying density estimation for the pairs of a pose es-timation result and the ground truth similarly as to themethod described in Section 3.3.

3.5 Pose Estimation θe

Finally we can estimate the object’s pose from twoviewpoints: the initial viewpoint and the next view-point. Here, I1 is the image observed from the initialviewpoint. After rotating δ [◦], we obtain I2, which isthe image observed from the next viewpoint.

We estimate the pose for these two viewpoints θeas the average of pose estimation results from I1 andI2 (by considering the rotation angle δ ) as

θe =φ1 +φ2−δ

2, (7)

where φ1 = f (I1) is the pose estimation from the ini-tial viewpoint and φ2 = f (I2) that from the next vie-wpoint.

4 EXPERIMENTS

4.1 Dataset

To show the effectiveness of the proposed viewpointrecommendation method, we performed a simulation-based evaluation. For the simulation, we used a set of3D models in the ShapeNet (Chang et al., 2015). Wecollected 135 models in the “Mug” class. Concretely,we put a 3D model in a virtual environment and ob-served it using a virtual depth sensor. By rotating thesensor around the z-axis of the 3D model, we obtai-ned 360 depth images in the range of [0◦,360◦) foreach model as shown in Figure 6.

Additionally, in the simulation, we changed theelevation angle of the virtual sensor as 0◦, 15◦, 30◦,


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0° 15° 30°

45° 60° 75°

Figure 7: Example of images observed from different ele-vation angles.

45◦, 60◦, and 75◦ which is elevated upright from thez-plane as shown in Figure 7. In total, 135 objectswere observed from each elevating angle with a totalof 48,600 images. We used the rendered images fortraining and testing in the evaluation. These imagesare divided into two folds: a training set and a testingset. Images of 100 objects are randomly selected forthe training set and the rest are used for the testing set.We conducted an experiment with the synthetic mugdataset, which we consider is adequate to address theambiguity issue for category-level pose estimation.

4.2 Evaluation Method

4.2.1 Pose Estimation Method

We prepared a network architecture similar to thePose-CyclicR-Net proposed by Ninomiya et al. (Ni-nomiya et al., 2017) as the pose estimator. The origi-nal network architecture is shown in Figure 8. Sincewe assumed that the object pose variation is limitedto a single axis rotation, we modified the network out-put to a pair of trigonometric functions (cosθ ,sinθ)instead of the original quaternion. We trained the poseestimator using the training images.

4.2.2 Evaluation Criteria

We evaluated how the recommended viewpoints areappropriate for the pose estimation by using severalcriteria. One criterion is the Mean Absolute Error(MAE) of the pose estimation results with the groundtruth. The pose estimation results are obtained byusing a pair of the initial viewpoint and the recom-mended viewpoint. By considering the circularity ofangles, the error can be calculated as

MAE =1N

N

∑i=1

d(θ ie,θ

ig), (8)

where N represents the number of images, θ ie and θ i

gare the pose estimation result and the ground truth,respectively. d(θ i

e,θg)i is the absolute difference of

the poses considering the circularity defined as

d(θe,θg) =

{|θe−θg| if |θe−θg|> 180◦,180◦−|θe−θg| otherwise.

(9)

The other criterion is Pose Estimation Accuracy(PEA). This criterion is be defined as

PEA(τ) =1N

N

∑i=1

F(d(θ ie,θ

ig)< τ), (10)

where τ represents a threshold error which reflectsthe difference of pose estimation result θ i

e and groundtruth θ i

g , F(·) is a function which returns 1 if the con-dition in the function holds and 0 vice versa.

4.2.3 Comparative Methods

We compared the pose estimation results by the pro-posed method and several other baseline methods. Tothe best of our knowledge, there is no existing met-hod that could be directly compared with our propo-sed method as the category-level next best viewpointestimation study is just initiated by us. Thus as a base-line, we used pose estimation from a single viewpoint,which just applies Pose-CyclicR-Net-like Network tothe input image. We also compared with several vie-wpoint recommendation methods.

We adapted two other baseline methods from(Sock et al., 2017) which are “Random” and “Furt-hest”. The first baseline viewpoint recommendationmethod is recommending the next viewpoint rand-omly, named “Random”. Because of the random-ness, we selected ten viewpoints randomly and avera-ged the estimation results. The second baseline view-point recommendation method is recommending thenext viewpoint by simply selecting the opposite sideor the furthest point from the initial viewpoint, named“Opposite” (equivalent to “Furthest”).

4.3 Results

4.3.1 Mean Absolute Error (MAE)

The experimental results are summarized in Table 1.Here, for all elevation angles, the proposed method


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Convolu

tion

Convolu

tion

Convolu

tion

Convolu

tion

Convolu

tion

Full

y-C

onnec

ted

𝒙in

𝑡

𝐸

Full

y-C

onnec

ted

𝑥1

𝑥2

𝑥3

𝑥4

𝑞1

𝑞2

𝑞3

𝑞4

𝑓1 𝑓2 𝑓3 𝑓4 𝑓5 𝑓6 𝑓7 𝒙out

Figure 8: Original Pose-CyclicR-Net (Ninomiya et al., 2017).

Table 1: Comparison of overall Pose Estimation Accuracy.

Elevation Single Random Opposite Proposedangle

0◦ 18.36◦ 16.34◦ 14.91◦ 14.18◦15◦ 15.55◦ 14.01◦ 13.35◦ 11.58◦30◦ 15.40◦ 13.87◦ 12.75◦ 11.96◦45◦ 10.71◦ 9.32◦ 8.81◦ 8.28◦60◦ 8.15◦ 7.27◦ 7.15◦ 6.31◦75◦ 7.36◦ 6.57◦ 6.36◦ 5.15◦

Figure 9: Pose Estimation Accuracy within threshold error0◦ – 100◦ (elevation angle = 15◦).

outperformed all other comparative methods. This re-sult clearly shows that the proposed method is promi-sing and gave a better way (next viewpoint) for objectpose estimation. We successfully managed to reducethe pose ambiguity in the difficult observation view-point which has been mentioned earlier in Figure 1at the beginning of this paper. We can see that esti-mating object’s pose from two viewpoints yields bet-ter results than from a single viewpoint. By compa-ring with the other pose recommendation methods,the proposed method achieved better results by care-fully selecting the best viewpoint for object pose esti-mation. By reducing the pose ambiguity, the proposedmethod achieved the lowest pose estimation error.

Table 2: Comparison using Partial-AUC (pAUC) between τfrom 0◦ to 60◦.

Elevation Single Random Opposite Proposedangle

0◦ 75.61 76.75 78.19 80.8015◦ 79.13 79.14 79.16 84.0530◦ 79.79 79.69 80.23 83.7545◦ 84.94 85.46 86.07 87.6560◦ 88.31 88.42 88.57 90.4975◦ 89.47 89.42 89.91 92.03

4.3.2 Pose Estimation Accuracy (PEA)

In Figure 9, we plotted the pose estimation accuracyby changing the threshold error τ in Equation 10 inthe case of elevation angle = 15◦. We confirmedthat the proposed method outperforms all the com-parison methods when the threshold error is within0◦ < τ < 60◦. When 60◦ < τ < 100◦, the “Opposite”method outperformed the proposed method. Howe-ver, a large threshold error value will not critically in-fluence the pose estimation accuracy, so we considerthe results when τ > 60◦ are not significant for thispurpose. We also calculated partial-AUCs (pAUCs)for all curves as summarized in Table 2. With all sixelevation angles, we showed that the proposed met-hod achieves the most accurate pose estimation com-pared to the other methods.

5 CONCLUSION

We proposed a new idea to estimate the best next vie-wpoint for an accurate pose estimation and a new fra-mework for minimizing the pose ambiguity. We sho-wed that the proposed method outperforms three ba-seline methods by utilizing the latent variable whichprovides us with a new next viewpoint in the category-level pose estimation. Therefore, by having this next


66

viewpoint, a reliable and high pose estimation accu-racy is achievable.

For future improvement, we are looking for-ward to evaluating the proposed method with multi-dimension elevation angles as to compare with ourcurrent single elevation angle implementation in thispaper. The improvement for obtaining a higherpose estimation accuracy by expanding the two vie-wpoints into several points has also been projectedas our upcoming task. To overcome the non-cascadenext viewpoint in specific cases, we also consider tohave “several” best next viewpoint where the multi-dimensional elevation angles are utilized. This appro-ach may be utilized with a different class of multipleobjects for having a wider scope of application andcould help the development of the human helper ro-bot field.

ACKNOWLEDGEMENT

The authors would like to thank Toyota Motor Corpo-ration, Ministry of Education of Government of Ma-laysia (MOE), and Universiti Teknikal Malaysia Me-laka (UTeM). Parts of this research were supported byMEXT, Grant-in-Aid for Scientific Research.

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