Performance improvement of segmentation-based depth representation in 3D imagery by region merging

The feasible implementation of immersive 3D video systems entails the need for a substantial reduction in the amount of image information necessary for representation. Multiview image rendering algorithms based on depth data have radically reduced the number of images required to reconstruct a 3D scene. Nonetheless, the compression of depth maps still poses several challenges due to the particular nature and characteristics of the data. To this end, this paper outlines a depth representation technique, developed in our earlier work, that exploits the correlation intrinsically present between color intensity and depth images capturing a natural scene. In this technique, a segmentation-based algorithm that is backwards compatible with conventional video coding systems is implemented. The effectiveness of our previous technique is enhanced in this contribution by a region merging process on the segmented regions, which results in a decrease in the amount of information necessary for transmission or storage of multiview image data by a factor of 20.5 with respect to the reference H.264/AVC coding methodology. This is furthermore achieved whilst maintaining a 3D image reconstruction and viewing quality which is quasi identical to the referenced approach.peer-reviewe

Livrable D4.2 of the PERSEE project : Représentation et codage 3D - Rapport intermédiaire - Définitions des softs et architecture

51Livrable D4.2 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.2 du projet. Son titre : Représentation et codage 3D - Rapport intermédiaire - Définitions des softs et architectur

FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic Neural Radiance Fields (NeRF). Despite its many advantages, this method suffers from artifacts introduced by the involved compression when combining it with recent state-of-the-art techniques for training the static per-frame NeRF models. In this paper, we perform an in-depth analysis of these artifacts and leverage the resulting insights to propose an improved representation. In particular, we present a novel density encoding that adapts the Fourier-based compression to the characteristics of the transfer function used by the underlying volume rendering procedure and leads to a substantial reduction of artifacts in the dynamic model. Furthermore, we show an augmentation of the training data that relaxes the periodicity assumption of the compression. We demonstrate the effectiveness of our enhanced Fourier PlenOctrees in the scope of quantitative and qualitative evaluations on synthetic and real-world scenes

Interpolation of Scientific Image Databases

This paper explores how recent convolutional neural network (CNN)-based techniques can be used to interpolate images inside scientific image databases. These databases are frequently used for the interactive visualization of large-scale simulations, where images correspond to samples of the parameter space (e.g., timesteps, isovalues, thresholds, etc.) and the visualization space (e.g., camera locations, clipping planes, etc.). These databases can be browsed post hoc along the sampling axis to emulate real-time interaction with large-scale datasets. However, the resulting databases are limited to their contained images, i.e., the sampling points. In this paper, we explore how efficiently and accurately CNN-based techniques can derive new images by interpolating database elements. We demonstrate on several real-world examples that the size of databases can be further reduced by dropping samples that can be interpolated post hoc with an acceptable error, which we measure qualitatively and quantitatively

STV-based Video Feature Processing for Action Recognition

In comparison to still image-based processes, video features can provide rich and intuitive information about dynamic events occurred over a period of time, such as human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last decade on image processing and seen its successful applications in face matching and object recognition, video-based event detection still remains one of the most difficult challenges in computer vision research due to its complex continuous or discrete input signals, arbitrary dynamic feature definitions, and the often ambiguous analytical methods. In this paper, a Spatio-Temporal Volume (STV) and region intersection (RI) based 3D shape-matching method has been proposed to facilitate the definition and recognition of human actions recorded in videos. The distinctive characteristics and the performance gain of the devised approach stemmed from a coefficient factor-boosted 3D region intersection and matching mechanism developed in this research. This paper also reported the investigation into techniques for efficient STV data filtering to reduce the amount of voxels (volumetric-pixels) that need to be processed in each operational cycle in the implemented system. The encouraging features and improvements on the operational performance registered in the experiments have been discussed at the end

Efficient multiview depth representation based on image segmentation

The persistent improvements witnessed in multimedia production have considerably augmented users demand for immersive 3D systems. Expedient implementation of this technology however, entails the need for significant reduction in the amount of information required for representation. Depth image-based rendering algorithms have considerably reduced the number of images necessary for 3D scene reconstruction, nevertheless the compression of depth maps still poses several challenges due to the peculiar nature of the data. To this end, this paper proposes a novel depth representation methodology that exploits the intrinsic correlation present between colour intensity and depth images of a natural scene. A segmentation-based approach is implemented which decreases the amount of information necessary for transmission by a factor of 24 with respect to conventional JPEG algorithms whilst maintaining a quasi identical reconstruction quality of the 3D views.peer-reviewe

Feature extraction on faces : from landmark localization to depth estimation

Le sujet de cette thèse porte sur les algorithmes d'apprentissage qui extraient les caractéristiques importantes des visages. Les caractéristiques d’intérêt principal sont des points clés; La localisation en deux dimensions (2D) ou en trois dimensions (3D) de traits importants du visage telles que le centre des yeux, le bout du nez et les coins de la bouche. Les points clés sont utilisés pour résoudre des tâches complexes qui ne peuvent pas être résolues directement ou qui requièrent du guidage pour l’obtention de performances améliorées, telles que la reconnaissance de poses ou de gestes, le suivi ou la vérification du visage. L'application des modèles présentés dans cette thèse concerne les images du visage; cependant, les algorithmes proposés sont plus généraux et peuvent être appliqués aux points clés de d'autres objets, tels que les mains, le corps ou des objets fabriqués par l'homme. Cette thèse est écrite par article et explore différentes techniques pour résoudre plusieurs aspects de la localisation de points clés. Dans le premier article, nous démêlons l'identité et l'expression d'un visage donné pour apprendre une distribution à priori sur l'ensemble des points clés. Cette distribution à priori est ensuite combinée avec un classifieur discriminant qui apprend une distribution de probabilité indépendante par point clé. Le modèle combiné est capable d'expliquer les différences dans les expressions pour une même représentation d'identité. Dans le deuxième article, nous proposons une architecture qui vise à conserver les caractéristiques d’images pour effectuer des tâches qui nécessitent une haute précision au niveau des pixels, telles que la localisation de points clés ou la segmentation d’images. L’architecture proposée extrait progressivement les caractéristiques les plus grossières dans les étapes d'encodage pour obtenir des informations plus globales sur l’image. Ensuite, il étend les caractéristiques grossières pour revenir à la résolution de l'image originale en recombinant les caractéristiques du chemin d'encodage. Le modèle, appelé Réseaux de Recombinaison, a obtenu l’état de l’art sur plusieurs jeux de données, tout en accélérant le temps d’apprentissage. Dans le troisième article, nous visons à améliorer la localisation des points clés lorsque peu d'images comportent des étiquettes sur des points clés. En particulier, nous exploitons une forme plus faible d’étiquettes qui sont plus faciles à acquérir ou plus abondantes tel que l'émotion ou la pose de la tête. Pour ce faire, nous proposons une architecture permettant la rétropropagation du gradient des étiquettes les plus faibles à travers des points clés, ainsi entraînant le réseau de localisation des points clés. Nous proposons également une composante de coût non supervisée qui permet des prédictions de points clés équivariantes en fonction des transformations appliquées à l'image, sans avoir les vraies étiquettes des points clés. Ces techniques ont considérablement amélioré les performances tout en réduisant le pourcentage d'images étiquetées par points clés. Finalement, dans le dernier article, nous proposons un algorithme d'apprentissage permettant d'estimer la profondeur des points clés sans aucune supervision de la profondeur. Nous y parvenons en faisant correspondre les points clés de deux visages en les transformant l'un vers l'autre. Cette transformation nécessite une estimation de la profondeur sur un visage, ainsi que une transformation affine qui transforme le premier visage au deuxième. Nous démontrons que notre formulation ne nécessite que la profondeur et que les paramètres affines peuvent être estimés avec un solution analytique impliquant les points clés augmentés par profondeur. Même en l'absence de supervision directe de la profondeur, la technique proposée extrait des valeurs de profondeur raisonnables qui diffèrent des vraies valeurs de profondeur par un facteur d'échelle et de décalage. Nous démontrons des applications d'estimation de profondeur pour la tâche de rotation de visage, ainsi que celle d'échange de visage.This thesis focuses on learning algorithms that extract important features from faces. The features of main interest are landmarks; the two dimensional (2D) or three dimensional (3D) locations of important facial features such as eye centers, nose tip, and mouth corners. Landmarks are used to solve complex tasks that cannot be solved directly or require guidance for enhanced performance, such as pose or gesture recognition, tracking, or face verification. The application of the models presented in this thesis is on facial images; however, the algorithms proposed are more general and can be applied to the landmarks of other forms of objects, such as hands, full body or man-made objects. This thesis is written by article and explores different techniques to solve various aspects of landmark localization. In the first article, we disentangle identity and expression of a given face to learn a prior distribution over the joint set of landmarks. This prior is then merged with a discriminative classifier that learns an independent probability distribution per landmark. The merged model is capable of explaining differences in expressions for the same identity representation. In the second article, we propose an architecture that aims at uncovering image features to do tasks that require high pixel-level accuracy, such as landmark localization or image segmentation. The proposed architecture gradually extracts coarser features in its encoding steps to get more global information over the image and then it expands the coarse features back to the image resolution by recombining the features of the encoding path. The model, termed Recombinator Networks, obtained state-of-the-art on several datasets, while also speeding up training. In the third article, we aim at improving landmark localization when only a few images with labelled landmarks are available. In particular, we leverage a weaker form of data labels that are easier to acquire or more abundantly available such as emotion or head pose. To do so, we propose an architecture to backpropagate gradients of the weaker labels through landmarks, effectively training the landmark localization network. We also propose an unsupervised loss component which makes equivariant landmark predictions with respect to transformations applied to the image without having ground truth landmark labels. These techniques improved performance considerably when we have a low percentage of labelled images with landmarks. Finally, in the last article, we propose a learning algorithm to estimate the depth of the landmarks without any depth supervision. We do so by matching landmarks of two faces through transforming one to another. This transformation requires estimation of depth on one face and an affine transformation that maps the first face to the second one. Our formulation, which only requires depth estimation and affine parameters, can be estimated as a closed form solution of the 2D landmarks and the estimated depth. Even without direct depth supervision, the proposed technique extracts reasonable depth values that differ from the ground truth depth values by a scale and a shift. We demonstrate applications of the estimated depth in face rotation and face replacement tasks

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras