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Author |
Gabriel Villalonga |
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Title |
Leveraging Synthetic Data to Create Autonomous Driving Perception Systems |
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Year |
2021 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Manually annotating images to develop vision models has been a major bottleneck
since computer vision and machine learning started to walk together. This has
been more evident since computer vision falls on the shoulders of data-hungry
deep learning techniques. When addressing on-board perception for autonomous
driving, the curse of data annotation is exacerbated due to the use of additional
sensors such as LiDAR. Therefore, any approach aiming at reducing such a timeconsuming and costly work is of high interest for addressing autonomous driving
and, in fact, for any application requiring some sort of artificial perception. In the
last decade, it has been shown that leveraging from synthetic data is a paradigm
worth to pursue in order to minimizing manual data annotation. The reason is
that the automatic process of generating synthetic data can also produce different
types of associated annotations (e.g. object bounding boxes for synthetic images
and LiDAR pointclouds, pixel/point-wise semantic information, etc.). Directly
using synthetic data for training deep perception models may not be the definitive
solution in all circumstances since it can appear a synth-to-real domain shift. In
this context, this work focuses on leveraging synthetic data to alleviate manual
annotation for three perception tasks related to driving assistance and autonomous
driving. In all cases, we assume the use of deep convolutional neural networks
(CNNs) to develop our perception models.
The first task addresses traffic sign recognition (TSR), a kind of multi-class
classification problem. We assume that the number of sign classes to be recognized
must be suddenly increased without having annotated samples to perform the
corresponding TSR CNN re-training. We show that leveraging synthetic samples of
such new classes and transforming them by a generative adversarial network (GAN)
trained on the known classes (i.e. without using samples from the new classes), it is
possible to re-train the TSR CNN to properly classify all the signs for a ∼ 1/4 ratio of
new/known sign classes. The second task addresses on-board 2D object detection,
focusing on vehicles and pedestrians. In this case, we assume that we receive a set
of images without the annotations required to train an object detector, i.e. without
object bounding boxes. Therefore, our goal is to self-annotate these images so
that they can later be used to train the desired object detector. In order to reach
this goal, we leverage from synthetic data and propose a semi-supervised learning
approach based on the co-training idea. In fact, we use a GAN to reduce the synthto-real domain shift before applying co-training. Our quantitative results show
that co-training and GAN-based image-to-image translation complement each
other up to allow the training of object detectors without manual annotation, and still almost reaching the upper-bound performances of the detectors trained from
human annotations. While in previous tasks we focus on vision-based perception,
the third task we address focuses on LiDAR pointclouds. Our initial goal was to
develop a 3D object detector trained on synthetic LiDAR-style pointclouds. While
for images we may expect synth/real-to-real domain shift due to differences in
their appearance (e.g. when source and target images come from different camera
sensors), we did not expect so for LiDAR pointclouds since these active sensors
factor out appearance and provide sampled shapes. However, in practice, we have
seen that it can be domain shift even among real-world LiDAR pointclouds. Factors
such as the sampling parameters of the LiDARs, the sensor suite configuration onboard the ego-vehicle, and the human annotation of 3D bounding boxes, do induce
a domain shift. We show it through comprehensive experiments with different
publicly available datasets and 3D detectors. This redirected our goal towards the
design of a GAN for pointcloud-to-pointcloud translation, a relatively unexplored
topic.
Finally, it is worth to mention that all the synthetic datasets used for these three
tasks, have been designed and generated in the context of this PhD work and will
be publicly released. Overall, we think this PhD presents several steps forward to
encourage leveraging synthetic data for developing deep perception models in the
field of driving assistance and autonomous driving. |
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Address |
February 2021 |
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Thesis |
Ph.D. thesis |
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Publisher  |
Ediciones Graficas Rey |
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Editor |
Antonio Lopez;German Ros |
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978-84-122714-2-3 |
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Notes |
ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ Vil2021 |
Serial |
3599 |
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Author |
Gemma Rotger |
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Title |
Lifelike Humans: Detailed Reconstruction of Expressive Human Faces |
Type |
Book Whole |
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Year |
2021 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
Developing human-like digital characters is a challenging task since humans are used to recognizing our fellows, and find the computed generated characters inadequately humanized. To fulfill the standards of the videogame and digital film productions it is necessary to model and animate these characters the most closely to human beings. However, it is an arduous and expensive task, since many artists and specialists are required to work on a single character. Therefore, to fulfill these requirements we found an interesting option to study the automatic creation of detailed characters through inexpensive setups. In this work, we develop novel techniques to bring detailed characters by combining different aspects that stand out when developing realistic characters, skin detail, facial hairs, expressions, and microexpressions. We examine each of the mentioned areas with the aim of automatically recover each of the parts without user interaction nor training data. We study the problems for their robustness but also for the simplicity of the setup, preferring single-image with uncontrolled illumination and methods that can be easily computed with the commodity of a standard laptop. A detailed face with wrinkles and skin details is vital to develop a realistic character. In this work, we introduce our method to automatically describe facial wrinkles on the image and transfer to the recovered base face. Then we advance to facial hair recovery by resolving a fitting problem with a novel parametrization model. As of last, we develop a mapping function that allows transfer expressions and microexpressions between different meshes, which provides realistic animations to our detailed mesh. We cover all the mentioned points with the focus on key aspects as (i) how to describe skin wrinkles in a simple and straightforward manner, (ii) how to recover 3D from 2D detections, (iii) how to recover and model facial hair from 2D to 3D, (iv) how to transfer expressions between models holding both skin detail and facial hair, (v) how to perform all the described actions without training data nor user interaction. In this work, we present our proposals to solve these aspects with an efficient and simple setup. We validate our work with several datasets both synthetic and real data, prooving remarkable results even in challenging cases as occlusions as glasses, thick beards, and indeed working with different face topologies like single-eyed cyclops. |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Felipe Lumbreras;Antonio Agudo |
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978-84-122714-3-0 |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ Rot2021 |
Serial |
3513 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution texture classification of ceramic tiles. |
Type |
Book Chapter |
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Year |
1999 |
Publication |
Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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India |
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Notes |
ADAS;CIC |
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Call Number |
ADAS @ adas @ LBV1999b |
Serial |
45 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
Petia Radeva;JuanJose Villanueva |
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ADAS |
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Call Number |
Admin @ si @ Tol2001 |
Serial |
166 |
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Author |
Antonio Lopez |
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Title |
Multilocal Methods for Ridge and Valley Delineation in Image Analysis. |
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Year |
2000 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
Joan Serrat |
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ADAS |
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ADAS @ adas @ Lop2000 |
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174 |
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Author |
Felipe Lumbreras |
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Title |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
Type |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS |
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Call Number |
ADAS @ adas @ Lum2001 |
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188 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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Year |
2004 |
Publication |
3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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ADAS @ adas @ SAG2004a |
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458 |
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Author |
Angel Sappa; Fadi Dornaika |
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Title |
An Edge-Based Approach to Motion Detection |
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2006 |
Publication |
6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570 |
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Reading (United Kingdom) |
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ADAS |
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Call Number |
ADAS @ adas @ SaD2006 |
Serial |
654 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm |
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Year |
2006 |
Publication |
21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541 |
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Istanbul (Turkey) |
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ADAS |
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ADAS @ adas @ DoS2006d |
Serial |
688 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
Rigid and Non-Rigid Face Motion Tracking by Aligning Texture Maps and Stereo-Based 3D Models |
Type |
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Year |
2006 |
Publication |
8th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS´06), LNCS 4179: 675–684 |
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Antwerp (Belgium) |
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ADAS |
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ADAS @ adas @ DoS2006c |
Serial |
689 |
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