3D shape template generation from RGB-D images capturing
a moving and deforming object
Hikari Takehara, Yuta Nakashima, Tomokazu Sato, Naokazu Yokoya
Graduate School of Information Science, Nara Institute of Science and Technology
8916-5 Takayamacho, Ikoma, Nara, Japan

Abstract
Automatically reconstructing a 3D shape model of a nonrigid object using a sequence from a single commodity RGB-D
sensor is a challenging problem. Some techniques use a 3D shape
template of a target object; however, in order to generate the template automatically, the target object required to be stationary.
Otherwise, a non-rigid ICP algorithm, which registers a pair ofpoint clouds, can be used for reconstructing 3D geometry of a
non-rigid object directly, but it often fails due to the ambiguity inpoint correspondences. This paper presents a method for generating a 3D shape template from a single RGB-D sequence. In order to reduce the ambiguity in point correspondences, our method
leverages point trajectories obtained in the RGB images, which
can be used for associating points in different point clouds. We
demonstrate the capability of our method using deforming humanbodies.

3D shape template

Non-rigidtransformation

Figure 1: Template points (red) and point trajectories (black).

Introduction
Recently, various applications that present a moving and deforming object to users, such as virtual fitting room [1] and virtual
pets [2], have become available to ordinary users. These applications render objects based on 3D geometry of their entire shapes(which we refer to as full-body shape models) and their non-rigid
motion, both of which are usually handcrafted. Automatic techniques for reconstructing full-body shape models at each frame
can drastically reduce the cost for creating a 3D shape model andmotion (e.g., [3, 4]). They use multiple sensors (e.g., RGB or
RGB-D sensors), whose relative poses are known, to capture theobject from different viewpoints simultaneously. It then applies
an existing 3D reconstruction technique for rigid objects, such as[5, 6, 7]. However, the use of multiple sensors may be still cumbersome for some applications in which ordinary users need theirown shape models and motions.
Reconstructing 3D shape and motion from a single sensor isa challenging problem. Two approaches have been proposed: oneuses 3D shape templates of the target object and the other doesnot. Former approach [8, 9] generates a 3D shape template using
a 3D shape reconstruction technique for rigid objects [5, 6, 7],
assuming the object is almost stationary. They then fit the 3D
shape template to a 3D point cloud at each frame of a single depth
map sequence. One major limitation of this approach is that it
requires an extra burden to capture the target object while it isstationary, which is practically infeasible, especially for objects
like animals.
Latter approach [10, 11] registers 3D point clouds in all
frame of a single depth map sequence to any other frames using
non-rigid iterative closest point (ICP) [12, 13]; however, it often

fails due to the ambiguity in point correspondences and the highdegrees of freedom in non-rigid transformation. In addition, nonrigid ICP is formulated as a non-convex optimization problem andthus depends on initial values of transformation parameters.
This paper presents a novel non-rigid point cloud registration
method for 3D shape template generation, designed for an single
RGB-D sensor. Observing that the RGB image sequence contains rich cues for finding the correspondences between a pair ofpoints on an image and another, our method reduces the ambiguity
in point correspondences via optical flow-based point trajectories
obtained by, e.g., [14, 15]. Since the point trajectories terminate
when they disappear, assuming that the sequence captures the object from all around it, local descriptors (e.g., [16, 17, 18]) loosely
associate the point trajectories in different frames to reduce registration errors.
Point cloud registration in our method is based on the nonrigid ICP algorithms by Li et al. [12] and Amberg et al. [13],which directly register point clouds assuming non-rigid transformation. In order to incorporate the point trajectories obtained
from the RGB image sequence into these algorithms, we introduce template points, each of which is a point on the 3D shape
template associated with a point trajectory. The correspondencesamong 3D points in different point clouds are represented by the
associated template point as shown in Fig. 1. Our method thus
registers the point clouds via the template points. We formulate
this non-rigid registration as an optimization problem to find thetemplate points as well as non-rigid transformations from the template points to each point cloud. Due to its non-convexity, ourmethod still depends on initial values of the parameters, and wealleviate this by adopting two step optimization.
The main contributions of this paper are as follows.
• Our method uses the RGB image sequence for explicitly
identifying point correspondences. To the best of our knowledge, no prior work uses the RGB image sequence for the
purpose of non-rigid registration. This method well suits forcommodity RGB-D sensors, such as Microsoft Kinect.
• Our two step optimization provides stable registration withless dependency on the initial parameter values by gradually increasing the degrees of freedom of the transformation
assumed in registration.

Related work
This section reviews the relevant work in full-body 3D shapereconstruction of non-rigid objects.
Multiple sensors. Shape reconstruction using multiple sensors basically is a registration problem for rigid objects if the sensors are synchronized. Starck et al. [3] designed a system witheight calibrated RGB sensors, whose relative poses are known.
Their system reconstructs the 3D shape of non-rigid objects bycombining shape-from-silhouette and multi-view stereo. Dou etal. [4] presented a full-body scanning system with eight calibrated
RGB-D sensors. It relies on non-rigid registration [19] in order tomake the system more robust against unsynchronized sensors andmeasurement noises. Ye et al. [20] developed a motion capturingsystem with three hand-held RGB-D sensors. Since the poses ofthese sensors are unknown, it registers point clouds from them using ICP. These systems with multiple sensors can reconstruct the
full-body shape of target objects at each frame solely from observations but requires synchronization among the sensors and theirrelative poses.
Single sensor with prior knowledge. Full-body reconstruction with a single sensor is generally more challenging than themultiple sensor case because it needs to handle unobserved partsof the target object due to, e.g., occlusion.
Some techniques use various types prior knowledge to facilitate shape reconstruction from a single sensor. Prior knowledge
includes skeletons of articulated objects [21, 22], statistical shapemodels [23, 24], have been proposed. Malleson et al. [21] proposed a full-body shape reconstruction method designed for human body. The point cloud is divided into several partial point
clouds, so that each of them can be handled by rigid registration, which is followed by non-rigid registration. Schmidt et al.’s
method [22] reconstructs 3D shapes of articulated objects in realtime using GPU accelerated optimization. Anguelov et al. [23] fit
a partial 3D point cloud to a full-body shape model of human witha trained dataset of various shapes and poses. Chen et al. [24] acquire higher quality 3D shapes of non-rigid objects than that by
Anguelov et al.’s method [23] using a tensor-based deformation
model. These approaches are effective for specific objects, suchas a human; however, they cannot be applied to other objects.
Single sensor without prior knowledge. Some methods directly register 3D point cloud in every frame to any other frame[10, 11, 12, 13, 25, 26, 27]. Li et al. [12] and Amberg et al. [13]proposed a non-rigid ICP algorithm, which can handle larger deformation than Brown et al.’s [25]. Dou et al. [10] acquired highquality 3D shape of non-rigid objects, using the non-rigid ICP
and bundle adjustment algorithms. Newcombe et al.[11] pre-sented real-time non-rigid 3D shape reconstruction with GPUaccelerated optimization. This type of approaches can reconstructfull-body shapes from a single RGB-D sequence; however, it often fails due to the ambiguity in point correspondences and thehigh degrees of freedom of non-rigid transformation. In addition, such algorithms are formulated as a non-convex optimization
problem and thus are dependent on initial values of transformation
parameters.
Another approach for single-sensor 3D shape reconstruction
is to generate a full-body 3D shape beforehand (namely, a 3D
shape template) and fit it to a point cloud at each frame [8, 9]. Li
et al. [8] fit a low resolution 3D shape template to a point cloudand update it for finer geometric details. The 3D shape templateis generated by an existing reconstruction method for rigid object(e.g., [5, 6, 7]) assuming the object is almost stationary. Zollhöfer
et al. [9] presented a real-time method, which is similar to Li etal.’s [8] using GPU-accelerated optimization. These method canreconstruct full-body shape models more stably than the methods without shape templates; however, it requires a capturing step
dedicated for template generation, which can be practically impossible when the target is, for example, an animal.
Most existing methods with a single RGB-D sensor do notleverage RGB images. Our method provides stable non-rigid registration of point clouds using the RGB image sequence for explicitly identifying point correspondences.

Template generation using point trajectories
Given an RGB-D image sequence capturing a moving anddeforming object from entire directions around it, our method
first tracks the 2D point trajectories in RGB images using Sundaram et al.’s method [14] and then extract 3D point trajectories,
each of which is a series of 3D points in each point cloud givenby the depth maps. The i-th 3D point trajectory is denoted by
Xi = {xti |t = tis , . . . ,tie }, where tis and tie are the frame indexes
at which the point trajectory starts and terminates. The template
point associated with Xi is denoted by pi ∈ P, where P is the setof template points. We assume that xti and pi is associated by local
affine transformation identified by Ati and bti (i.e., pi = Ati xti + bti ).
Under this assumption we formulate the non-rigid registration
problem as a minimization problem of an energy function withrespect to pi and (Ati ,bti ) for all i and t. The point trajectory is
lost when the corresponding point is occluded, potentially resulting in erroneous registration. To alleviate this, we also use local
descriptor-based point correspondences in RGB images to loosely
associate point trajectories in different frames.

Local descriptor-based point correspondences
Our method uses Sundaram et al.’s method [14] to obtainpoint trajectories, which is based on optical flow in RGB images
sequence. This method can track relatively dense points over alonger period; however it cannot re-identify the same point once it
is lost. To remedy this problem, we utilize point correspondencesby SIFT feature points and local descriptors [16].
To obtain reliable point correspondences, as shown in Fig. 2,
we first find point correspondences between a target frame t andthe rest, and identify the frames whose number of point correspondences exceed a certain threshold TC . Since the sensor captures the target object from all around it, the number of the point
correspondences usually gives several sets of continuous frames.

𝑡𝑡′number of point
correspondences

2500

𝐱1𝑡

2000

𝐱 𝑡3

1500

𝐲𝑗𝑡

1000

𝐩5

𝐱 𝑡2

500

𝐩1

𝐩3
𝐩𝟔
𝐲𝑗𝑡

𝐩4

′

𝐱 𝑡6

′
𝐲𝑗𝑡

′

𝐱 𝑡4
𝐲𝑗𝑡

′

′

𝐱 𝑡5

𝐩2

threshold 𝑇𝐶

0
0

100

200
frame number

Coordinate systemin frame 𝑡

300

Figure 2: Obtaining a frame pair (t,t 0 ) for finding local descriptorbased point correspondences.
For each set, we find the frame t 0 that gives the largest number ofpoint correspondences. We denote the set of all frame pairs (t,t 0 )obtained in this process by G .

Energy function
Let A and B the sets of linear and translation componentsof the affine transformations. Our energy function isE(P, A , B) = αF EF + αC EC + αR ER + αS ES ,(1)

where αF , αC , αR , and αS are weights to determine the contributions of terms EF , EC , ER , and ES , respectively.
Registration error term EF involves the registration error
defined by the sum of distances between the template points pi
and corresponding 3D trajectory points transformed by its associated affine transformation Ati xti + bti , i.e.,
EF (P, A , B) = ∑∑ ||pi − (Ati xti + bti )||22 ,(2)

t i∈V (t)where V (t) is the index set of point trajectory-based 3D points
observed in frame t. The smaller EF is, the closer the template
point and the transformed point are to each other.
Local descriptor-based error term EC penalizes large distances between pairs of local descriptor-based 3D points in different frames in the template point space, inspired by Li et al.’s
method [8]. Instead of defining a template point for each local
descriptor-based 3D point ytj as with point trajectory-based one,we choose to loosely associate ytj with several pi ’s so that the
point correspondences by local descriptors can directly affects
pi ’s. For this, we represent ytj ’s corresponding point in the template space by a weighted sum of template points associated with
3D points xti around ytj as shown in Fig. 3, assuming that the spatial relationship among ytj and neighboring xti ’s is preserved in thetemplate point space, i.e.,ỹtj =

w ji pi ,∑(3)i∈M (t, j)

where M (t, j) is the index set of 3D points xti in frame t that are
m nearest neighbors of ytj , and w ji is a weight determined by the
distance between ytj and xti . We calculate w ji using Eq. (1) in [8].
This leads our local descriptor-based error term
EC (P) =0∑ ∑ ||ỹtj − ỹtj ||22 ,(t,t 0 )∈G j(4)

Coordinate systemin template

Coordinate systemin frame 𝑡′

Figure 3: Local descriptor-based 3D point ytj and its representation in the template point space.
where G is the set of frame pairs described in the previous section. This term implies that the local descriptor-based 3D points
in frames t and s should be close enough to each other in the template space as they represent the same point on the object.
Regularization terms ER and ES are introduced to constraintransformation parameters A and B, assuming that the target object is articulated as in Li et al. [8]. Motions of most points on
an articulated object can be described by rigid transformations;
therefore, ER constrains the linear component Ati to be almost arotation matrix, which can be represented by∑ ||(Ati )T Ati − I||2F ,ER (A ) = ∑(5)t i∈V (t)where || • ||F is the Frobenius norm.
In addition, because the motions of neighboring points are
similar to each other under rigid motions, a 3D point xti transformed by its own transformation (Ati ,bti ) and one of its neighbor’s transformation (Atj ,btj ) must be close to each other in thetemplate point space. This can be encoded by
ES (A , B) = ∑ ∑ ∑ ||Ati xti + bti − (Atj xti + btj )||22 ,
t

i

(6)

j

where the second and third summations are calculated for i ∈ V (t)and j ∈ N (t, i), respectively, and N (t, i) is the index set of nnearest neighbors of the 3D point xti .

Two-step optimization
Our proposed method minimizes the energy function in
Eq. (1), which is a non-convex optimization problem because ER
is a quartic function with respect to each element of Ati . The solution thus depends on initial values of P, A , and B. To alleviate
this dependency, we adopt two-step optimization based on ourheuristics.
In the first step, we assume that all xti ’s share the same transformation, i.e., Ati = At and bti = bt for all i. Under this assumption, the number of free parameters is drastically reduced. Also
ES is identically 0 and thus Eq. (1) can be rewritten to
E 0 (P, A , B) = αF0 EF0 + αR0 ER0 + αC0 EC ,

(7)

where
EF0 (P, A , B)=

∑ ∑ ||pi − (At xti + bt )||22 ,t i∈V (t)(8)

Figure 4: Examples of masked RGB images and depth images in DS1 (left) and DS2 (right).

(b) DS2(a) DS1

Figure 5: Obtained template points in first (left) and second (center) steps, and 3D shape template (right) in second step for DS1 and DS2.
ER0 (A )=∑ ||(At )T At − I||2F .

(9)tαF0 , αR0 , and αC0 are weights. The proposed method minimizes
E 0 using the gradient descent method. Since ER0 is still a quartic
function, this minimization is again a non-convex problem; however, our preliminary study has demonstrated that it gives goodinitial estimates of parameter values for the later step.
In the second step, Eq. (1) is minimized with initial estimatesof the parameter values obtained in the first step, again using thegradient decent method.

Experimental results
Implementation and dataset
We heuristically determined the threshold value TC = 80.
The weight values for the first step optimization αF0 , αR0 , and αC0
were set to 1, 100, and 0.1, respectively, and for the second step
αF = 0.1, αR = 1.0. Those for the second step optimization were
αC = 1.0, αS = 1.0. The numbers m and n of nearest neighbors
in M and N were both set to 4.
For the first step optimization, we set all the template points
pi to 0 as initial values. For At and bt , we use the parameters of rigid transformations as their initial values for fast convergence. To obtain the rigid transformation, we registered point
clouds from consecutive two RGB-D images sequentially, assuming that the transformation between two point clouds can be described by a rigid transformation. We then calculated the transformation from each frame’s point cloud to, e.g., the first frame’s
one, by recursively multiplying the transformations.
In this experiment, we generated the 3D shape template from
two RGB-D sequences, i.e., DS1 capturing a human moving his

body and head, and DS2 capturing a human moving his arms, inwhich the numbers of frames are 350 and 325, respectively. They
were captured using fixed Microsoft Kinect v2. The subjects rotated in front of the sensor while capturing. We consider that thisis almost equivalent to moving the sensor. Since the captured images included background regions, we extracted the human regions by thresholding the depth values, as shown in Fig. 4.

Results
The template points and shape templates by the first and second optimization steps are shown in Fig. 5, where the mesh models were generated by using an algorithm based on the Poisson
formulation [28].
For DS1, the template points around the head converged in
the second step optimization compared with those after the firststep. This demonstrates that our energy function with local affine
transformations works properly. On the other hand, the pointaround the neck spread after the second step. One of possible
reasons is that the local descriptor-based correspondences were
erroneous around the neck (i.e., some points around neck were associated with those around the shoulder). For DS2, the template
points around the right hand, which was divided into two parts,
got closer because of the loose correspondences by local descriptors. However, particularly for forearms, the numbers of point trajectories and local descriptor-based point correspondences wererelatively small, and thus the template points did not exhibit good
convergence.
To quantitatively evaluate the accuracy of the proposed
method, we transformed the template points to the sensor’s coordinate system during the first and second optimization steps,
and then calculated the distance between each transformed tem-First step

Second stepFirst step

Second step(b) DS2(a) DS1

Figure 6: Examples of template points (black) transformed back to the sensor’s coordinate system in a certain frame and the point
trajectory-based 3D points in that frame (red) in first and second steps for DS1 and DS2.
plate point and its associated point trajectory-based 3D point using the inverse transformation of Ati and bti , as shown in Fig. 6.
Fig. 7 shows the average and largest distances as well as the distances’ 5th and 95th percentile for DS1 and DS2. These results
indicate that our two-step optimization works well, decreasing the
distances in both steps, although the largest distance did not decrease in the second step.
For better 3D shape templates, we need removal of wrong
correspondences by local descriptors as well as uniformly extracted point trajectories. The geodesic distance-based weights
for Eq. (3) may also improve the local descriptor-based point correspondences, especially for the neck and the forearms in DS1
and DS2, respectively.

Conclusion
In this paper, we have proposed a method for 3D shape
template generation leveraging RGB image sequence obtained
while capturing depth images with an RGB-D sensor. For stability, we have also proposed the two-step optimization strategy,
which gradually increases the degree of freedom of transformations assumed in the energy function. Our experimental results
have demonstrated that the proposed method can generate the 3D
shape template with about 0.01 meter of the averaged distance between a transformed template point and its associated 3D point.
Our future work includes the improvement of the quality of shape
template by, e.g., removing wrong local descriptor-based point
correspondences and using geodesic distances to determine theweights in Eq. (3).

Acknowledgements
This work was supported in part by JSPS KAKENHI No.
25540086.

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Average
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102
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Figure 7: The evolution of average distance, largest distance, 5th and 95th percentile value of distance in first (a)(c) and second (b)(d)steps for DS1 and DS2.
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Author Biography
Hikari Takehara received his B.E. in electrical and electronic engineering from the National Institute of Technology, Kumamoto in 2013 andhis M.E. in information science from Nara Institute of Science and Technology (NAIST) in 2015. He is currently pursuing his Ph.D. at NAIST. His
main research interests include computer vision and computer graphics.
Yuta Nakashima received his B.E. and M.E. degrees in communication engineering from Osaka University, Osaka, Japan in 2006 and 2008,
respectively, and the Ph.D. degree in engineering from Osaka University,
Osaka, Japan, in 2012. He is currently an assistant professor at Graduate
School of Information Science, Nara Institute of Science and Technology(NAIST). He was a research fellow of the Japan Society for the Promo-

tion of Science (JSPS) from 2010 to 2012, and was a Visiting Scholar at
the University of North Carolina at Charlotte in 2012. His research interests include video content analysis using probabilistic and statistical
approaches. He is a member of the IEEE, the ACM, and the IEICE.
Tomokazu Sato received his B.E. degree in computer and system science from Osaka Prefecture University in 1999. He received his M.E. and
Ph.D. degrees in information science from Nara Institute of Science andTechnology (NAIST) in 2001 and 2003, respectively. He was an assistantprofessor at NAIST in 2003-2011. He was a visiting researcher at Czech
Technical University in Prague in 2010-2011. He has been an associate
professor at NAIST since 2011. He is a member of IEEE, IEICE, IPSJ,VRSJ and ITE.
Naokazu Yokoya received his B.E., M.E., and Ph.D. degrees in information and computer sciences from Osaka University in 1974, 1976,
and 1979, respectively. He joined Electrotechnical Laboratory (ETL) of
the Ministry of International Trade and Industry in 1979. He was a visiting professor at McGill University in Montreal in 1986-87 and has been aprofessor at Nara Institute of Science and Technology (NAIST) since 1992.
He has also been a vice president at NAIST since April 2013. He is a fellow of IPSJ, IEICE and VRSJ and a member of IEEE, ACM SIGGRAPH,
JSAI, JCSS and ITE.