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Giovanni Maria Farinella, Petia Radeva, Jose Braz, & Kadi Bouatouch. (2021). Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Volume 4) (Vol. 4).
Abstract: This book contains the proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), endorsed by the International Association for Pattern Recognition (IAPR), and in cooperation with the ACM Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH), the European Association for Computer Graphics (EUROGRAPHICS), the EUROGRAPHICS Portuguese Chapter, the VRVis Center for Virtual Reality and Visualization Forschungs-GmbH, the French Association for Computer Graphics (AFIG), and the Society for Imaging Science and Technology (IS&T). The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences. VISIGRAPP 2021 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. This year VISIGRAPP was, exceptionally, held as a web-based event, due to the COVID-19 pandemic, from 8 – 10 February. We received a high number of paper submissions for this edition of VISIGRAPP, 371 in total, with contributions from 52 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 12 papers from GRAPP, 8 from HUCAPP, 11 papers from IVAPP, and 56 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 24% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 67 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes three plenary keynote lectures, given by internationally distinguished researchers, namely Federico Tombari (Google and Technical University of Munich, Germany), Dieter Schmalstieg (Graz University of Technology, Austria) and Nathalie Henry Riche (Microsoft Research, United States), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2021 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the Springer Nature Computer Science journal. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for VISAPP. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Finally, we gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for VISIGRAPP 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. We wish you all an exciting conference. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org
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Giovanni Maria Farinella, Petia Radeva, Jose Braz, & Kadi Bouatouch. (2021). Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications – (Volume 5) (Vol. 5).
Abstract: This book contains the proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) which was organized and sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC), endorsed by the International Association for Pattern Recognition (IAPR), and in cooperation with the ACM Special Interest Group on Graphics and Interactive Techniques (SIGGRAPH), the European Association for Computer Graphics (EUROGRAPHICS), the EUROGRAPHICS Portuguese Chapter, the VRVis Center for Virtual Reality and Visualization Forschungs-GmbH, the French Association for Computer Graphics (AFIG), and the Society for Imaging Science and Technology (IS&T). The proceedings here published demonstrate new and innovative solutions and highlight technical problems in each field that are challenging and worthy of being disseminated to the interested research audiences. VISIGRAPP 2021 was organized to promote a discussion forum about the conference’s research topics between researchers, developers, manufacturers and end-users, and to establish guidelines in the development of more advanced solutions. This year VISIGRAPP was, exceptionally, held as a web-based event, due to the COVID-19 pandemic, from 8 – 10 February. We received a high number of paper submissions for this edition of VISIGRAPP, 371 in total, with contributions from 52 countries. This attests to the success and global dimension of VISIGRAPP. To evaluate each submission, we used a hierarchical process of double-blind evaluation where each paper was reviewed by two to six experts from the International Program Committee (IPC). The IPC selected for oral presentation and for publication as full papers 12 papers from GRAPP, 8 from HUCAPP, 11 papers from IVAPP, and 56 papers from VISAPP, which led to a result for the full-paper acceptance ratio of 24% and a high-quality program. Apart from the above full papers, the conference program also features 118 short papers and 67 poster presentations. We hope that these conference proceedings, which are submitted for indexation by Thomson Reuters Conference Proceedings Citation Index, SCOPUS, DBLP, Semantic Scholar, Google Scholar, EI and Microsoft Academic, will help the Computer Vision, Imaging, Visualization, Computer Graphics and Human-Computer Interaction communities to find interesting research work. Moreover, we are proud to inform that the program also includes three plenary keynote lectures, given by internationally distinguished researchers, namely Federico Tombari (Google and Technical University of Munich, Germany), Dieter Schmalstieg (Graz University of Technology, Austria) and Nathalie Henry Riche (Microsoft Research, United States), thus contributing to increase the overall quality of the conference and to provide a deeper understanding of the conference’s interest fields. Furthermore, a short list of the presented papers will be selected to be extended into a forthcoming book of VISIGRAPP Selected Papers to be published by Springer during 2021 in the CCIS series. Moreover, a short list of presented papers will be selected for publication of extended and revised versions in a special issue of the Springer Nature Computer Science journal. All papers presented at this conference will be available at the SCITEPRESS Digital Library. Three awards are delivered at the closing session, to recognize the best conference paper, the best student paper and the best poster for each of the four conferences. There is also an award for best industrial paper to be delivered at the closing session for VISAPP. We would like to express our thanks, first of all, to the authors of the technical papers, whose work and dedication made it possible to put together a program that we believe to be very exciting and of high technical quality. Next, we would like to thank the Area Chairs, all the members of the program committee and auxiliary reviewers, who helped us with their expertise and time. We would also like to thank the invited speakers for their invaluable contribution and for sharing their vision in their talks. Finally, we gratefully acknowledge the professional support of the INSTICC team for all organizational processes, especially given the need to introduce online streaming, forum management, direct messaging facilitation and other web-based activities in order to make it possible for VISIGRAPP 2021 authors to present their work and share ideas with colleagues in spite of the logistic difficulties caused by the current pandemic situation. We wish you all an exciting conference. We hope to meet you again for the next edition of VISIGRAPP, details of which are available at http://www. visigrapp.org.
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Vacit Oguz Yazici. (2022). Towards Smart Fashion: Visual Recognition of Products and Attributes (Joost Van de Weijer, & Arnau Ramisa, Eds.). Ph.D. thesis, IMPRIMA, .
Abstract: Artificial intelligence is innovating the fashion industry by proposing new applications and solutions to the problems encountered by researchers and engineers working in the industry. In this thesis, we address three of these problems. In the first part of the thesis, we tackle the problem of multi-label image classification which is very related to fashion attribute recognition. In the second part of the thesis, we address two problems that are specific to fashion. Firstly, we address the problem of main product detection which is the task of associating correct image parts (e.g. bounding boxes) with the fashion product being sold. Secondly, we address the problem of color naming for multicolored fashion items. The task of multi-label image classification consists in assigning various concepts such as objects or attributes to images. Usually, there are dependencies that can be learned between the concepts to capture label correlations (chair and table classes are more likely to co-exist than chair and giraffe).
If we treat the multi-label image classification problem as an orderless set prediction problem, we can exploit recurrent neural networks (RNN) to capture label correlations. However, RNNs are trained to predict ordered sequences of tokens, so if the order of the predicted sequence is different than the order of the ground truth sequence, there will be penalization although the predictions are correct. Therefore, in the first part of the thesis, we propose an orderless loss function which will order the labels in the ground truth sequence dynamically in a way that the minimum loss is achieved. This results in a significant improvement of RNN models on multi-label image classification over the previous methods.
However, RNNs suffer from long term dependencies when the cardinality of set grows bigger. The decoding process might stop early if the current hidden state cannot find any object and outputs the termination token. This would cause the remaining classes not to be predicted and lower recall metric. Transformers can be used to avoid the long term dependency problem exploiting their selfattention modules that process sequential data simultaneously. Consequently, we propose a novel transformer model for multi-label image classification which surpasses the state-of-the-art results by a large margin.
In the second part of thesis, we focus on two fashion-specific problems. Main product detection is the task of associating image parts with the fashion product that is being sold, generally using associated textual metadata (product title or description). Normally, in fashion e-commerces, products are represented by multiple images where a person wears the product along with other fashion items. If all the fashion items in the images are marked with bounding boxes, we can use the textual metadata to decide which item is the main product. The initial work treated each of these images independently, discarding the fact that they all belong to the same product. In this thesis, we represent the bounding boxes from all the images as nodes in a fully connected graph. This allows the algorithm to learn relations between the nodes during training and take the entire context into account for the final decision. Our algorithm results in a significant improvement of the state-ofthe-art.
Moreover, we address the problem of color naming for multicolored fashion items, which is a challenging task due to the external factors such as illumination changes or objects that act as clutter. In the context of multi-label classification, the vaguely defined lines between the classes in the color space cause ambiguity. For example, a shade of blue which is very close to green might cause the model to incorrectly predict the color blue and green at the same time. Based on this, models trained for color naming are expected to recognize the colors and their quantities in both single colored and multicolored fashion items. Therefore, in this thesis, we propose a novel architecture with an additional head that explicitly estimates the number of colors in fashion items. This removes the ambiguity problem and results in better color naming performance.
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Hugo Bertiche, Meysam Madadi, & Sergio Escalera. (2021). PBNS: Physically Based Neural Simulation for Unsupervised Garment Pose Space Deformation. In 14th ACM Siggraph Conference and exhibition on Computer Graphics and Interactive Techniques in Asia.
Abstract: We present a methodology to automatically obtain Pose Space Deformation (PSD) basis for rigged garments through deep learning. Classical approaches rely on Physically Based Simulations (PBS) to animate clothes. These are general solutions that, given a sufficiently fine-grained discretization of space and time, can achieve highly realistic results. However, they are computationally expensive and any scene modification prompts the need of re-simulation. Linear Blend Skinning (LBS) with PSD offers a lightweight alternative to PBS, though, it needs huge volumes of data to learn proper PSD. We propose using deep learning, formulated as an implicit PBS, to unsupervisedly learn realistic cloth Pose Space Deformations in a constrained scenario: dressed humans. Furthermore, we show it is possible to train these models in an amount of time comparable to a PBS of a few sequences. To the best of our knowledge, we are the first to propose a neural simulator for cloth.
While deep-based approaches in the domain are becoming a trend, these are data-hungry models. Moreover, authors often propose complex formulations to better learn wrinkles from PBS data. Supervised learning leads to physically inconsistent predictions that require collision solving to be used. Also, dependency on PBS data limits the scalability of these solutions, while their formulation hinders its applicability and compatibility. By proposing an unsupervised methodology to learn PSD for LBS models (3D animation standard), we overcome both of these drawbacks. Results obtained show cloth-consistency in the animated garments and meaningful pose-dependant folds and wrinkles. Our solution is extremely efficient, handles multiple layers of cloth, allows unsupervised outfit resizing and can be easily applied to any custom 3D avatar.
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Swathikiran Sudhakaran, Sergio Escalera, & Oswald Lanz. (2021). Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, .
Abstract: We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance.
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Pau Riba, Sounak Dey, Ali Furkan Biten, & Josep Llados. (2021). Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild.
Abstract: This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.
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Josep Llados. (2021). The 5G of Document Intelligence. In 3rd Workshop on Future of Document Analysis and Recognition.
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Shiqi Yang, Yaxing Wang, Joost Van de Weijer, Luis Herranz, & Shangling Jui. (2021). Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation. In Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021).
Abstract: Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might no longer align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors, and propose a self regularization loss to decrease the negative impact of noisy neighbors. Furthermore, to aggregate information with more context, we consider expanded neighborhoods with small affinity values. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. Code is available in https://github.com/Albert0147/SFDA_neighbors.
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Wenjuan Gong, Zhang Yue, Wei Wang, Cheng Peng, & Jordi Gonzalez. (2022). Meta-MMFNet: Meta-Learning Based Multi-Model Fusion Network for Micro-Expression Recognition. ACMTMC - ACM Transactions on Multimedia Computing, Communications, and Applications, .
Abstract: Despite its wide applications in criminal investigations and clinical communications with patients suffering from autism, automatic micro-expression recognition remains a challenging problem because of the lack of training data and imbalanced classes problems. In this study, we proposed a meta-learning based multi-model fusion network (Meta-MMFNet) to solve the existing problems. The proposed method is based on the metric-based meta-learning pipeline, which is specifically designed for few-shot learning and is suitable for model-level fusion. The frame difference and optical flow features were fused, deep features were extracted from the fused feature, and finally in the meta-learning-based framework, weighted sum model fusion method was applied for micro-expression classification. Meta-MMFNet achieved better results than state-of-the-art methods on four datasets. The code is available at https://github.com/wenjgong/meta-fusion-based-method.
Keywords: Feature Fusion; Model Fusion; Meta-Learning; Micro-Expression Recognition
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Mohamed Ramzy Ibrahim, Robert Benavente, Felipe Lumbreras, & Daniel Ponsa. (2022). 3DRRDB: Super Resolution of Multiple Remote Sensing Images using 3D Residual in Residual Dense Blocks. In CVPR 2022 Workshop on IEEE Perception Beyond the Visible Spectrum workshop series (PBVS, 18th Edition).
Abstract: The rapid advancement of Deep Convolutional Neural Networks helped in solving many remote sensing problems, especially the problems of super-resolution. However, most state-of-the-art methods focus more on Single Image Super-Resolution neglecting Multi-Image Super-Resolution. In this work, a new proposed 3D Residual in Residual Dense Blocks model (3DRRDB) focuses on remote sensing Multi-Image Super-Resolution for two different single spectral bands. The proposed 3DRRDB model explores the idea of 3D convolution layers in deeply connected Dense Blocks and the effect of local and global residual connections with residual scaling in Multi-Image Super-Resolution. The model tested on the Proba-V challenge dataset shows a significant improvement above the current state-of-the-art models scoring a Corrected Peak Signal to Noise Ratio (cPSNR) of 48.79 dB and 50.83 dB for Near Infrared (NIR) and RED Bands respectively. Moreover, the proposed 3DRRDB model scores a Corrected Structural Similarity Index Measure (cSSIM) of 0.9865 and 0.9909 for NIR and RED bands respectively.
Keywords: Training; Solid modeling; Three-dimensional displays; PSNR; Convolution; Superresolution; Pattern recognition
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Razieh Rastgoo, Kourosh Kiani, Sergio Escalera, Vassilis Athitsos, & Mohammad Sabokrou. (2022). All You Need In Sign Language Production.
Abstract: Sign Language is the dominant form of communication language used in the deaf and hearing-impaired community. To make an easy and mutual communication between the hearing-impaired and the hearing communities, building a robust system capable of translating the spoken language into sign language and vice versa is fundamental.
To this end, sign language recognition and production are two necessary parts for making such a two-way system. Signlanguage recognition and production need to cope with some critical challenges. In this survey, we review recent advances in
Sign Language Production (SLP) and related areas using deep learning. To have more realistic perspectives to sign language, we present an introduction to the Deaf culture, Deaf centers, psychological perspective of sign language, the main differences between spoken language and sign language. Furthermore, we present the fundamental components of a bi-directional sign language translation system, discussing the main challenges in this area. Also, the backbone architectures and methods in SLP are briefly introduced and the proposed taxonomy on SLP is presented. Finally, a general framework for SLP and performance evaluation, and also a discussion on the recent developments, advantages, and limitations in SLP, commenting on possible lines for future research are presented.
Keywords: Sign Language Production; Sign Language Recog- nition; Sign Language Translation; Deep Learning; Survey; Deaf
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Bojana Gajic, Ariel Amato, Ramon Baldrich, Joost Van de Weijer, & Carlo Gatta. (2022). Area Under the ROC Curve Maximization for Metric Learning. In CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition).
Abstract: Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification.
Keywords: Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition
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Javad Zolfaghari Bengar, Joost Van de Weijer, Laura Lopez-Fuentes, & Bogdan Raducanu. (2022). Class-Balanced Active Learning for Image Classification. In Winter Conference on Applications of Computer Vision.
Abstract: Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active learning is generally studied on balanced datasets where an equal amount of images per class is available. However, real-world datasets suffer from severe imbalanced classes, the so called long-tail distribution. We argue that this further complicates the active learning process, since the imbalanced data pool can result in suboptimal classifiers. To address this problem in the context of active learning, we proposed a general optimization framework that explicitly takes class-balancing into account. Results on three datasets showed that the method is general (it can be combined with most existing active learning algorithms) and can be effectively applied to boost the performance of both informative and representative-based active learning methods. In addition, we showed that also on balanced datasets
our method 1 generally results in a performance gain.
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Jon Almazan, Bojana Gajic, Naila Murray, & Diane Larlus. (2018). Re-ID done right: towards good practices for person re-identification.
Abstract: Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.
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Akhil Gurram. (2022). Monocular Depth Estimation for Autonomous Driving (Antonio Lopez, & Onay Urfalioglu, Eds.). Ph.D. thesis, IMPRIMA, .
Abstract: 3D geometric information is essential for on-board perception in autonomous driving and driver assistance. Autonomous vehicles (AVs) are equipped with calibrated sensor suites. As part of these suites, we can find LiDARs, which are expensive active sensors in charge of providing the 3D geometric information. Depending on the operational conditions for the AV, calibrated stereo rigs may be also sufficient for obtaining 3D geometric information, being these rigs less expensive and easier to install than LiDARs. However, ensuring a proper maintenance and calibration of these types of sensors is not trivial. Accordingly, there is an increasing interest on performing monocular depth estimation (MDE) to obtain 3D geometric information on-board. MDE is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Moreover, a set of single cameras with MDE capabilities would still be a cheap solution for on-board perception, relatively easy to integrate and maintain in an AV.
Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Accordingly, the overall goal of this PhD is to study methods for improving CNN-based MDE accuracy under different training settings. More specifically, this PhD addresses different research questions that are described below. When we started to work in this PhD, state-of-theart methods for MDE were already based on CNNs. In fact, a promising line of work consisted in using image-based semantic supervision (i.e., pixel-level class labels) while training CNNs for MDE using LiDAR-based supervision (i.e., depth). It was common practice to assume that the same raw training data are complemented by both types of supervision, i.e., with depth and semantic labels. However, in practice, it was more common to find heterogeneous datasets with either only depth supervision or only semantic supervision. Therefore, our first work was to research if we could train CNNs for MDE by leveraging depth and semantic information from heterogeneous datasets. We show that this is indeed possible, and we surpassed the state-of-the-art results on MDE at the time we did this research. To achieve our results, we proposed a particular CNN architecture and a new training protocol.
After this research, it was clear that the upper-bound setting to train CNN-based MDE models consists in using LiDAR data as supervision. However, it would be cheaper and more scalable if we would be able to train such models from monocular sequences. Obviously, this is far more challenging, but worth to research. Training MDE models using monocular sequences is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. To alleviate these problems, we perform MDE by virtual-world supervision and real-world SfM self-supervision. We call our proposalMonoDEVSNet. We compensate the SfM self-supervision limitations by leveraging
virtual-world images with accurate semantic and depth supervision, as well as addressing the virtual-to-real domain gap. MonoDEVSNet outperformed previous MDE CNNs trained on monocular and even stereo sequences. We have publicly released MonoDEVSNet at <https://github.com/HMRC-AEL/MonoDEVSNet>.
Finally, since MDE is performed to produce 3D information for being used in downstream tasks related to on-board perception. We also address the question of whether the standard metrics for MDE assessment are a good indicator for future MDE-based driving-related perception tasks. By using 3D object detection on point clouds as proxy of on-board perception, we conclude that, indeed, MDE evaluation metrics give rise to a ranking of methods which reflects relatively well the 3D object detection results we may expect.
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