Efficient Monocular Pose Estimation for Complex 3D Models
A. Rubio, M. Villamizar, L. Ferraz, A. Penate-Sanchez,
A. Ramisa, E. Simo-Serra, A. Sanfeliu and F. Moreno-Noguer
Institut de Robòtica i Informàtica Industrial, CSIC-UPC
Llorens Artigas 4-6, 08028 Barcelona, Spain

Abstract— We propose a robust and efficient method toestimate the pose of a camera with respect to complex 3D
textured models of the environment that can potentially containmore than 100, 000 points. To tackle this problem we follow a
top down approach where we combine high-level deep networkclassifiers with low level geometric approaches to come up witha solution that is fast, robust and accurate. Given an input
image, we initially use a pre-trained deep network to computea rough estimation of the camera pose. This initial estimate
constrains the number of 3D model points that can be seenfrom the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the modeland the 2D detected image features. Accurate pose estimation
is finally obtained from the 2D-to-3D correspondences using anovel PnP algorithm that rejects outliers without the need to use
a RANSAC strategy, and which is between 10 and 100 times
faster than other methods that use it. Two real experiments
dealing with very large and complex 3D models demonstrate
the effectiveness of the approach.

I. I NTRODUCTION
Robust camera localization is a fundamental problem ina wide range of robotics applications, going from precise
object manipulation to autonomous vehicle navigation. Despite being a topic researched for decades it is still an open
challenge. There exist approaches based on infrared cameras
and high-frequency systems such as Vicon [1], which haveshown excellent results for localization and navigation ofrobots. However, these systems are limited to indoor environments where lighting conditions are controlled.
Another alternative to robustly localize the robot is toequip it with multiple sensors, such as lasers or stereo
cameras, and then fusing the data from each of them.
Although these systems increase the reliability of the robot
for self-localization, they have a negative impact on thecomputational cost and payload. This is specially critical insome robotics tasks where small and low-cost robots (e.g.
aerial robots) are frequently used.
In contrast to these multi-sensor approaches, in this work
we propose an efficient and robust system based uniquely ona monocular camera and a known pre-computed 3D textured
model of the environment, as shown in Fig. 1. Indeed, theproposed method is able to efficiently estimate the full pose(rotation and translation) of the camera within the 3D map,
given solely one single input image. No temporal information
is used about the previous poses that can constrain the regionof the 3D model where the camera is pointing to. While thismakes our problem significantly more complex, it makes theresulting pose estimation robust to issues such as drifting,Fig. 1: Problem definition: Given an input image (a) anda known textured 3D model of the environment (b), theproblem is to estimate the pose of the camera that captured
the image with respect to the model (c). The main challenge
addressed in this paper is to perform the correspondence ofpoints efficiently and reliably for complex 3D that containa large number of points. In this example, the model of the
Sagrada Familia has over 100,000 points.
occlusions of the model during short periods of time, or
sudden camera motions.
More precisely, let us assume our 3D model is madeof n 3D points, each associated to a visual descriptor
representing its appearance. Fig. 2 shows the overall schemeof the proposed method. In our case these visual descriptors
correspond to SIFT features [12] obtained from a set oftraining images previously used, in an off-line step, to build
the model (Fig. 2(g)). At runtime, the descriptors of the 3Dmodel are compared against the m descriptors extracted fromthe input image (Fig. 2(d)) in order to determine a first set
of 3D-to-2D match candidates, to then estimate the pose
(Fig 1(c)). However, solving this correspondence problem
has an O(n · m) complexity, which is extremely costly if we
consider that n can be very large (e.g. 100,000 points).
In order to alleviate the computational load, we propose
including a preliminary step based on a deep-learning network that yields an approximate initial pose, without theneed to explicitly compute correspondences. This method
provides the k most similar images (among the training
images used to build the 3D model) to the input image(Fig. 2(e)). Then, the descriptors of these images are used
to perform the 3D-to-2D matching (Fig. 2(f)). Therefore, thecorrespondence is done with a small part of the model but notwith the complete model. And most importantly, this initial

(a)

(e)

(b)
Deep Network

(g)

(c)

Input image

Image representation(d)Feature detectionand extraction (SIFT)

k-nearest images

3D model of the object(Images and SIFT features)(h)(f)
Matching

REPPnP

R,tFig. 2: Overall scheme of the proposed method for efficient camera pose estimation using highly complex models.
pre-selection of most similar images is done in a matter ofmilliseconds. Since these correspondences may still containfalse matches, we get rid of them by using REPPnP [4],a novel RANSAC-less algorithm for rejecting outliers. The
experiments have shown that the proposed method not only
reduces the computation cost but that it also achieves highaccuracy rates in highly complex models.
The rest of the paper describes each components of the
proposed method. Sec. III explains the construction of the
3D model. Sec. IV describes the initial pose estimation
using the deep-learning network and the REPPnP algorithm
used for the subsequent refinement. In Sec. V the method
is extensively evaluated over two different models. Finally,
Sec. VI summarizes the main contributions and future work.
II. R ELATED W ORK
Methods for 3D pose estimation from monocular images
can be roughly split into two categories: Geometric approaches that rely on local image features (e.g., points)
and use geometric relations to compute the pose; andAppearance-based methods that compute global descriptorsof the image, and then use machine learning approaches toestimate which image within the training set is the closest
one to a given input image.
Geometric approaches use local descriptors to estimate
2D-to-3D matches between one input image and one orseveral reference images registered to a 3D model. PnP
algorithms such as EPnP [10], [13] are then used to enforce
geometric constraints and solve for the pose parameters. On
top of that, robust RANSAC-based strategies [2], [14] canbe used both to speed up the matching process and to filter
outlier correspondences. Yet, while these methods provide
very accurate results, they require both the reference andinput images to be of high quality, such that local features
can be reliably and repetitively extracted. Additionally, if thenumber of points is very large, the outlier rejection scheme
can become extremely slow. Recently, [23] has shown thatpriors about the orientation of the camera relative to theground plane can speed up this process. We do not consider
these kind of priors for our method, though.
On the other hand, approaches relying on global descriptions of the image are less sensitive to a precise localizationof individual features. These methods typically use a set oftraining images acquired from different viewpoints to statistically model the spatial relationship of the local features,
either using one single detector for all poses [7], [11], [22] or
a combination of various pose-specific detectors [15], [16],[24], [26], [27]. Another alternative is to bind image featureswith poses during training and have them vote in the posespace [6]. The limitation of these approaches is that theestimated pose tends to be innacurate, and highly depends
on the spatial resolution at which the training images havebeen acquired. The highest granularity of the training set,the more precise can be the estimated pose, although theresulting detector is more prone to give false positives.
In this paper we combine the best of both worlds. On
the one hand, we will use a global descriptor based on a
deep convolutional network to get a first estimation of thepose. Deep networks have recently shown impressive resultsin image classification tasks [9]. This initial estimate will in
its turn reduce the number of potential 2D-to-3D matches,and make geometric approaches applicable. On the geometricside, we will make use of a very recent PnP approach,which inherently incorporates an outlier rejection schemewithout the need to run RANSAC [5]. The combinationsof both ingredients will result in a powerful pose estimation
algorithm capable of dealing with very large models.
III. B UILDING THE 3D MODEL
Our approach assumes a 3D textured model of the scene
to be available. To build these models we used Bundler [20],a structure-from-motion system for unordered image collections. Given a set of N images of the scene we seek to model,
this package initially extracts SIFT descriptors of all images,
and then simultaneously estimates the N camera poses and
3D structure using bundle adjustment. This is usually a time
consuming process that can take a few hours.
For each 3D point of the model, Bundler provides its SIFT
descriptor, its 3D position, its RGB color and the numberof images where the point appears. Also, for each image it
gives the calibration matrix, the rotation matrix, R, and thetranslation vector, t. This data will be used as “ground truth”
when evaluating the precision of the algorithm.
For this paper, we used two 3D models: The Sagrada
Familia dataset (from [17]), composed of 478 images ofthis church in Barcelona. The resulting 3D model contains

Fig. 3: Courtyard 3D model built and two sample images.
100, 532 3D points. Fig. 1(b) shows the model, and plots
in yellow the retrieved camera pose for each one of the N
training set images. The Courtyard of the mathematics school
dataset is composed of 265 images, which resulted in a 3Dmodel with 30, 196 points. The model and a two sample
images are shown in Fig. 3. Note that while the amount ofpoints in the first model is larger, its images present lessdifference in terms of visual appearance.
IV. M ERGING A PPEARANCE AND G EOMETRIC M ETHODS
We next describe how appearance and geometric methods are combined in a top-down coarse-to-fine approach,to tackle the problem of pose estimation from very large
models. Let us first formulate our problem:
Assume we are given a 3D model with n points{p1 , . . . , pn } and N training images {T1 , . . . , TN } whichhave been used to compute the 3D model. Each of theseimages has an associated pose {Ri , ti }. Each point pi has
an associated SIFT descriptor and a visibility list vi with theindexes of the training images from where it is seen. Given
an input image I, our goal is to accurately compute the posefrom where it was acquired.
One straightforward solution would be to extract m 2Dfeatures {u1 , . . . , um } from the input image I and compute
2D-to-3D correspondences {ui ↔ pi } by just comparing
SIFT descriptors. This set of correspondences could then be
filtered using a RANSAC+PnP scheme to get rid of outliers
and accurately estimate the pose. Nonetheless, since we areconsidering cases where n >> m those correspondences areprone to contain a very large percentage of outliers, whichmight dramatically slow down the process.
In this work we propose a two stage strategy that combines
the so-called appearance and geometric methods. The former
will compute the subset of the training images which ismore similar to our input image. This will constrain theset of candidate poses. The latter will use this subset ofposes to limit the number of 3D points of the model thatare potentially visible, and refine the pose using a geometricapproach. These two steps are next discussed.
A. Coarse Pose Estimation
Given our input image I and training images{T1 , . . . , TN } we seek to design a fast and robust strategy
to get the k training images which are more similar to I.

For obtaining this subset we could represent the images
using any global image descriptor, e.g, bag of words, GIST,
and simply compare such descriptors.
In this paper, though, we have used the recent genericimage-level features obtained from a deep network for image
representation. In particular, we use the 4,096-dimensional
second to last layer of a Convolutional Neural Network(CNN) as our high-level image representation. The full
network has 5 convolutional layers followed by 3 fully
connected layers, and obtained the best performance in the
ILSVRC-2012 challenge. The network is trained on a subsetof ImageNet [3] to classify 1,000 different classes. We use
the publicly available implementation and pre-trained model
provided by [8]. The features obtained with this procedure
have been shown to generalize well and outperform traditional hand-crafted features, thus they are already being used
in a wide diversity of tasks [21].
Comparing the resulting vectors of this representation we
obtain, for each input image I, a subset of the k most“similar” training images {T1 , . . . , Tk }. These images may
or may not be clustered in a similar region of the space.
Indeed, the value of k is chosen sufficiently large to ensurethat the subset of “similar” images contains at least one
image which is actually close to I, i.e, the set of poses
associated to these images represents just a very roughestimation of the ground truth pose. We next explore themand refine the pose using a geometric approach.
B. Fine Pose Estimation
The k closest training images provide a coarse estimateof the camera pose. To increase the accuracy we adapted the
REPPnP method [4], which simultaneously allows to discard
outlier correspondences while estimates the camera pose.
Standard PnP approaches assume the 2D-to-3D correspondences to be free of outliers. Therefore, when dealing withreal images these methods need an outlier rejection preprocessing step (e.g. RANSAC + P3P), which may significantly
reduce the overall computational efficiency.
Given c 2D-to-3D correspondences {ui ↔ pi } and thecamera internal calibration matrix A, PnP methods build
upon the perspective constraint for each 2D feature point i,
 
 
p
ui
(1)
di
= A [R|t] i ,11
where di is the depth of the feature point.
REPPnP reformulates the previous equations as a lowrank homogeneous set of equations Mx = 0, where M isa 2c × 12 matrix representing the perspective constraints for
all c correspondences. In [4] is shown that the solutions x
can be estimated assuming that the rank of the null-spaceof M is equal to 1. Once x is estimated, the camera pose
[R|t] is solved using a generalization of the Orthogonal
Procrustes problem [18], combined with a projected gradients optimization. In REPPnP, the outlier rejection is doneby iterating the estimation of x. At each iteration, thoseequations in M that after being projected onto x provide thelowest algebraic errors are chosen. This procedure showskTime (s)

All
6
8
12
16

45.669412.3344
14.1634
16.9470
20.2644

All
3
6
8
12
16

50.291215.529621.2358
24.3889
28.9528
33.5211# 3D points # Matches
Sagrada Familia model
100532
470
24855
424
29046
436
35422
447
43005
452
Courtyard model
30196
379
6189
296
10038
322
12134
328
15152
334
18145
337

erot

etrans

0.01240.01780.01760.01770.01780.02310.03050.03060.03030.03060.02790.02550.02670.02120.02060.03030.04490.04410.0476
0.03430.03600.0538TABLE I: Performance of the proposed approach in the twomodels for different values of the parameter k. We also
consider the case when all the model points are considered.
convergence with up to 50% of outliers and requiring up
to two orders of magnitude less computational time thanstandard P3P + RANSAC + PnP algorithms.
In order to adapt REPPnP to the case of this paper withlarge scenarios, we propose building the M matrix as kdifferent parts, each coming from one of the k similar images
retrieved in the previous stage. By doing this we can reduce
drastically the number of outliers, to rates below the 50%,for which the REPPnP is shown to succeed. Concretely, we
propose to solve,
i>
h
>
>
>
x=0
(2)
M1 M2 ... Mk
where Mj for j = [1, 2, ..., k] represent the homogeneous
equations obtained as in [4] by matching the 2D points
ui with the 3D points pji which are visible (according tov, . . . , vk ) in each of the k nearest images.
With the proposed method, taking k = 6, we decrease
the number of 3D points to be matched to one quarterof the total in the Sagrada Familia scenario and to one
third in the Courtyard scenario, increasing the number of
inlier correspondences. Table I shows the performance of
our approach for both models and different values of k.
V. R ESULTS
In this section, the efficiency and accuracy of the proposed
method will be evaluated using the two models presented in
Sec.III. Previously, we will show how our image retrieval
algorithm performs on the test datasets.
For the experiments, both datasets have been evenly split
into two disjoint sets. One of them is used as training set forthe construction of the model, and the other one as testing
set for the evaluation of the method.
A. Image retrieval for Coarse Pose Estimation
In order to evaluate the quality of the image retrieval using
the deep network descriptors, we have plotted in Fig. 4 theEuclidean distance between the descriptors for each pairof test/train images in the Sagrada Familia dataset. For
each test image (row) the training images are ordered byangular distance of the rotation matrix. The accumulation ofbluish regions on the left hand side of the graph indicatesthat the distance between descriptors increases as we move

Fig. 4: Comparison of the L2 distances between distancedeep network features and the known angular distance
between the test images and training images. Each row issorted according to the angular distance. We can see a strong
correlation between images that are close to each other andtheir descriptors obtained from the deep network.

away from the original viewpoint, and thus confirms a highconfidence in the image retrieval process we use.
As illustrative examples, in Fig. 5 we show one sample
query for each dataset. We plot both the input image and theclosest images in the training set according to the distances
between the deep network descriptors. In both cases nearly
all selected images present similar poses to the test image.
B. Efficiency assessment
In order to have a clear picture of the cost of our method,
we have measured the computational time for each of thedifferent stages: SIFT feature extraction on the input image,
image retrieval through our deep learning approach, selectionof the corresponding SIFT descriptors from the model,
descriptor matching and outlier removal along with pose
estimation done by the REPPnP algorithm. To compare thecomputation time of the approach against a typical baseline,
we have replaced REPPnP in our pipeline, by RANSAC
combined with OPnP [28], one of the fastest and mostaccurate approaches in the state-of-the-art.
Average times over the complete test set are given in
Fig. 6. As can be observed, the coarse filtering of model
images reduces significantly the computation time of thegeometrical estimation of the camera pose.
It can also be seen in the figure that the most time
consuming step of the whole pipeline is by far the SIFT
descriptor matching. This step is performed using the MATLAB implementation of the VLFeat open source library [25].
It is, therefore, critical to obtain a good set of neighboring
images: the better it is, the less model descriptors will be
required in the geometrical estimation step. In our experiments, this coarse to fine approach led to 75% reduction of
the computational time, but it could be even more significant
on larger models.
Finally, in Table II we show the average time required
to compute the deep network descriptors and the bag ofvisual words from a new image (left), and the REPPnPFig. 5: Input image 422 of the Sagrada Familia model (top) and 18 of the Courtyard model (bottom), with similar images
selected by our image retrieval algorithm (k = 4 and k = 5) and a plot showing the distance to the training images.
Computation
Time
Time
COMPUTATIONTIME(Zoom)(ZOOM)COMPUTATIONTIME Computation
Matching
Input image SIFT
REPPNP
Near SIFT selection
Deep learning

25

0.2
0.15
Time (s)Time (s)

50

0.1
0.05

0

All

6

8
k

12

16

0

All

6

8
k

12

16

Fig. 6: Analysis of computation time. Left: Computation
time of the approaches with different values of k. The
case All is equivalent to not doing any appearance based
pre-computation over the 3D model. Right: Zoom of thecomputation times.
and RANSAC (right). While a deep network descriptor canbe extracted directly from an input image in a matter ofmilliseconds, the bag of visual words requires first computing
SIFT descriptors of that image (on the order of seconds,
depending on the size of the image), and then finding thecorresponding visual word for each descriptor with a precomputed dictionary. Regarding the geometrical estimation
part, by using REPPnP we are able to reduce the timerequired by a factor of 5.
C. Accuracy
The accuracy is computed as the rotation and the translation errors in the calculated pose. The rotation error
is estimated using quaternions as erot = kquat(R) −quat(Rtrue )k/kquat(Rtrue )k, and the translation error as
etrans = kt−ttrue k/kttrue k. The estimated pose is {R, t}, and{Rtrue , ttrue }, corresponds to the ground truth given by the
Bundler algorithm, as mentioned in Sec. III. As observed inFig. 7, the errors found using the coarse to fine approach arecomparable to the ones obtained with the complete model.
In all experiments, we obtain a significant reduction of theerror when compared to RANSAC+OPnP.
Fig. 8 shows a qualitative comparison of the reprojectionof the SIFT descriptors in the input image obtained with the
R and t given by REPPnP and RANSAC+OPnP, along withthe ground truth reprojection (given by Bundler). By using
REPPnP we obtain good results over all the datasets while in

Model

Bag of Words

CNN Features

Gain (%)

Sag. Familia
Courtyard

1.63
5.48

0.02080.0207

98.72
99.62

k

RANSAC+OPnP

REPPnP

Gain (%)

6
8
12
16

0.02170.01930.01880.01870.00470.00300.00300.0030

78.34
84.46
84.04
83.96

TABLE II: Time values in seconds for the different methods
evaluated. The upper part of the table compares the two
methods evaluated for the coarse estimation of the pose (Bagof words vs. Deep network features). The lower part reports
the computation time of the methods used for fine pose
estimation (RANSAC + OPnP vs. REPPnP).
contrast, RANSAC+OPnP, does not always provide a good
estimation in all images.
VI. C ONCLUSIONSThe time required to estimate pose on a large scale 3D
models has been significantly reduced using a method thatcombines a purely appearance based technique with a geometrical approach. By using first global image appearance
we reduce the number of matches to test but at the sametime by performing a PnP match over the candidate images
the error in our pose estimation becomes nearly negligible.
In this work we have shown how it is possible to leverage
deep learning techniques to improve appearance based approaches for the robotic community. It is remarkable that weare able to perform accurate pose estimation over hundredsof thousands of points in a few seconds per image, especiallyconsidering that we are using a MATLAB implementation.
As future work, we will consider the possibility of further
exploiting deep networks by replacing the ubiquitous SIFT
descriptor by descriptors learnt with CNNs [19].
VII. ACKNOWLEDGMENTS
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness under projects ERANet Chistera project ViSen PCIN-2013-047, PAU+ DPI201127510 and ROBOT-INT-COOP DPI2013-42458-P, and by
the EU project ARCAS FP7-ICT-2011-28761.

Fig. 7: Rotation and translation errors for different values of k nearest images for both models. All charts show, as areference, the corresponding error when estimating the pose with the whole model.

Rotation errors

(c)!

(a)
(b)
(c)
(d)

REPPnPRANSAC+OPnP

0.00680.00420.00330.00120.00070.08120.00240.0079Translation errors
(a)
(b)
(c)
(d)
(a)!

(b)!

REPPnPRANSAC+OPnP

0.03060.01090.00910.01090.03830.14970.00910.0267(d)!

Fig. 8: Examples of pose estimation results for the Sagrada Familia (left) and Courtyard (right) models. For each image
we show the reprojected 3D coordinates of the SIFT points using the ground truth R and t (from Bundler) and the ones
estimated by both REPPnP and RANSAC+OPnP with k = 6.

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