Unsupervised Monocular Depth Estimation with Left-Right Consistency

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Exploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora

Indirect 3D Reconstruction Through Appearance Prediction

As humans, we easily perceive shape and depth, which helps us navigate our environment and interact with objects around us. Automating these abilities for computers is critical for many applications such as self-driving cars, augmented reality or architectural surveying. While active 3D reconstruction methods, such as laser scanning or structured light can produce very accurate results, they are typically expensive and their use cases can be limited. In contrast, passive methods that make use of only easily captured photographs, are typically less accurate as mapping from 2D images to 3D is an under-constrained problem. In this thesis we will focus on passive reconstruction techniques. We explore ways to get 3D shape from images in two challenging situations: 1) where a collection of images features a highly specular surface whose appearance changes drastically between the images, and 2) where only one input image is available. For both cases, we pose the reconstruction task as an indirect problem. In the first situation, the rapid change in appearance of highly specular objects makes it infeasible to directly establish correspondences between images. Instead, we develop an indirect approach using a panoramic image of the environment to simulate reflections, and recover the surface which best predicts the appearance of the object. In the second situation, the ambiguity inherent in single-view reconstruction is typically solved with machine learning, but acquiring depth data for training is both difficult and expensive. We present an indirect approach, where we train a neural network to regress depth by performing the proxy task of predicting the appearance of the image when the viewpoint changes. We prove that highly specular objects can be accurately reconstructed in uncontrolled environments, producing results that are 30% more accurate compared to the initialisation surface. For single frame depth estimation, our approach improves object boundaries in the reconstructions and significantly outperforms all previously published methods. In both situations, the proposed methods shrink the accuracy gap between camera-based reconstruction versus what is achievable through active sensors

Digging Into Self-Supervised Monocular Depth Estimation

Per-pixel ground-truth depth data is challenging to acquire at scale. To overcome this limitation, self-supervised learning has emerged as a promising alternative for training models to perform monocular depth estimation. In this paper, we propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods. Research on self-supervised monocular training usually explores increasingly complex architectures, loss functions, and image formation models, all of which have recently helped to close the gap with fully-supervised methods. We show that a surprisingly simple model, and associated design choices, lead to superior predictions. In particular, we propose (i) a minimum reprojection loss, designed to robustly handle occlusions, (ii) a full-resolution multi-scale sampling method that reduces visual artifacts, and (iii) an auto-masking loss to ignore training pixels that violate camera motion assumptions. We demonstrate the effectiveness of each component in isolation, and show high quality, state-of-the-art results on the KITTI benchmark.Comment: ICCV 1

Heightfields for Efficient Scene Reconstruction for AR

3D scene reconstruction from a sequence of posed RGB images is a cornerstone task for computer vision and augmented reality (AR). While depth-based fusion is the foundation of most real-time approaches for 3D reconstruction, recent learning based methods that operate directly on RGB images can achieve higher quality reconstructions, but at the cost of increased runtime and memory requirements, making them unsuitable for AR applications. We propose an efficient learning-based method that refines the 3D reconstruction obtained by a traditional fusion approach. By leveraging a top-down heightfield representation, our method remains real-time while approaching the quality of other learning-based methods. Despite being a simplification, our heightfield is perfectly appropriate for robotic path planning or augmented reality character placement. We outline several innovations that push the performance beyond existing top-down prediction baselines, and we present an evaluation framework on the challenging ScanNetV2 dataset, targeting AR tasks

Etude de l'influence de la viscosité sur la distribution de taille de gouttes par mesure PDPA

International audienceLe diagnostic de Phase Doppler Anemometry est une technique de mesure largement utilisée pour étudier les distributions en taille et en vitesse de gouttes dans un spray, mais celui-ci est rarement appliqué sur des fluides visqueux car très sensible à la non sphéricité des gouttes formées. La présente étude se propose d’étudier les performances de ce diagnostic sur un injecteur industriel de type simplex fonctionnant avec deux carburants, du kérosène et un mélange huile/kérosène permettant de simuler la viscosité du kérosène en haute altitude.Couplée à ces mesures, la technique d’ombroscopie ultra rapide a été utilisée pour analyser la structure du spray, donner une indication sur les conditions à étudier et post-traiter les mesures de PDA donc le procédé de validation des mesures n’était pas satisfaisant

Constant Velocity Constraints for Self-Supervised Monocular Depth Estimation

We present a new method for self-supervised monocular depth estimation. Contemporary monocular depth estimation methods use a triplet of consecutive video frames to estimate the central depth image. We make the assumption that the ego-centric view progresses linearly in the scene, based on the kinematic and physical properties of the camera. During the training phase, we can exploit this assumption to create a depth estimation for each image in the triplet. We then apply a new geometry constraint that supports novel synthetic views, thus providing a strong supervisory signal. Our contribution is simple to implement, requires no additional trainable parameter, and produces competitive results when compared with other state-of-the-art methods on the popular KITTI corpus

A far-ultraviolet-driven photoevaporation flow observed in a protoplanetary disk

Most low-mass stars form in stellar clusters that also contain massive stars, which are sources of far-ultraviolet (FUV) radiation. Theoretical models predict that this FUV radiation produces photo-dissociation regions (PDRs) on the surfaces of protoplanetary disks around low-mass stars, impacting planet formation within the disks. We report JWST and Atacama Large Millimetere Array observations of a FUV-irradiated protoplanetary disk in the Orion Nebula. Emission lines are detected from the PDR; modelling their kinematics and excitation allows us to constrain the physical conditions within the gas. We quantify the mass-loss rate induced by the FUV irradiation, finding it is sufficient to remove gas from the disk in less than a million years. This is rapid enough to affect giant planet formation in the disk

SimpleRecon: 3D reconstruction without 3D convolutions

Traditionally, 3D indoor scene reconstruction from posed images happens in two phases: per-image depth estimation, followed by depth merging and surface reconstruction. Recently, a family of methods have emerged that perform reconstruction directly in final 3D volumetric feature space. While these methods have shown impressive reconstruction results, they rely on expensive 3D convolutional layers, limiting their application in resource-constrained environments. In this work, we instead go back to the traditional route, and show how focusing on high quality multi-view depth prediction leads to highly accurate 3D reconstructions using simple off-the-shelf depth fusion. We propose a simple state-of-the-art multi-view depth estimator with two main contributions: 1) a carefully-designed 2D CNN which utilizes strong image priors alongside a plane-sweep feature volume and geometric losses, combined with 2) the integration of keyframe and geometric metadata into the cost volume which allows informed depth plane scoring. Our method achieves a significant lead over the current state-of-the-art for depth estimation and close or better for 3D reconstruction on ScanNet and 7-Scenes, yet still allows for online real-time low-memory reconstruction. Code, models and results are available at https://nianticlabs.github.io/simplerecon

Influence de la variabilité des débits sur les taux d’érosions etle relief long-terme : l’exemple de la bordure sud-est du MassifCentral, France

National audienceL’évolution des paysages résulte de l’action conjointe des forçages tectoniques etclimatiques. Ces processus n’agissent pas de manières continus mais via desévénements ponctuels (séismes, glissements de terrains, crues majeurs) qui, intégréssur des temps longs conduisent à la formation des reliefs. L’incision fluviale contrôle ladénudation des paysages et, est souvent modélisée comme une fonction de la contraintede cisaillement et de la puissance du cours d’eau. Ce type de modèle (Stream PowerModel) exprime l’incision des rivières en fonction de l’aire de drainage et de la pente duchenal qui sont des variables facilement quantifiables à partir des donnéestopographiques. Néanmoins, il ne prend pas en compte certains paramètres tels que leseuil d’incision et la variabilité des débits, ce qui a nécessité des évolutions de cemodèle (Stochastic …) devant encore être validées par des données de terrain.La bordure sud-est du Massif Central est une zone intéressante pour étudier cesproblématiques car elle présente des épisodes de fortes précipitations concentrées surle relief, entraînant des différences marquées dans les distributions des débits. Noustestons ces modèles en quantifiant les taux d’érosion à l’aide des nucléidescosmogéniques (10Be), en caractérisant la variabilité des débits avec les stationshydrométriques et en effectuant une analyse morphologique des profils de rivières.L'analyse de 326 stations hydrométriques nous permet d'observer un fort gradient devariabilité des débits depuis la bordure SE jusqu'à l'intérieur du massif. Lesconcentrations en 10Be mesurées dans les sédiments des rivières de 34 bassins versantsimpliquent une grande variation des taux d’érosion entre 29 et 126 mm/ka. Nouscomparons ces taux avec diverses paramètres hydro-morphologiques et, intégrons cesobservations dans le cadre des modèles. Nos résultats confirment l’existence desrelations non linéaires entre les taux de dénudation et le steepness index et leurdépendance à la variabilité hydrologique et au runoff

Influence de la variabilité des débits sur les taux d’érosions etle relief long-terme : l’exemple de la bordure sud-est du MassifCentral, France

National audienceL’évolution des paysages résulte de l’action conjointe des forçages tectoniques etclimatiques. Ces processus n’agissent pas de manières continus mais via desévénements ponctuels (séismes, glissements de terrains, crues majeurs) qui, intégréssur des temps longs conduisent à la formation des reliefs. L’incision fluviale contrôle ladénudation des paysages et, est souvent modélisée comme une fonction de la contraintede cisaillement et de la puissance du cours d’eau. Ce type de modèle (Stream PowerModel) exprime l’incision des rivières en fonction de l’aire de drainage et de la pente duchenal qui sont des variables facilement quantifiables à partir des donnéestopographiques. Néanmoins, il ne prend pas en compte certains paramètres tels que leseuil d’incision et la variabilité des débits, ce qui a nécessité des évolutions de cemodèle (Stochastic …) devant encore être validées par des données de terrain.La bordure sud-est du Massif Central est une zone intéressante pour étudier cesproblématiques car elle présente des épisodes de fortes précipitations concentrées surle relief, entraînant des différences marquées dans les distributions des débits. Noustestons ces modèles en quantifiant les taux d’érosion à l’aide des nucléidescosmogéniques (10Be), en caractérisant la variabilité des débits avec les stationshydrométriques et en effectuant une analyse morphologique des profils de rivières.L'analyse de 326 stations hydrométriques nous permet d'observer un fort gradient devariabilité des débits depuis la bordure SE jusqu'à l'intérieur du massif. Lesconcentrations en 10Be mesurées dans les sédiments des rivières de 34 bassins versantsimpliquent une grande variation des taux d’érosion entre 29 et 126 mm/ka. Nouscomparons ces taux avec diverses paramètres hydro-morphologiques et, intégrons cesobservations dans le cadre des modèles. Nos résultats confirment l’existence desrelations non linéaires entre les taux de dénudation et le steepness index et leurdépendance à la variabilité hydrologique et au runoff