OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, reconstruction, and generation in the real world, we propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties: 1) Large Vocabulary: It comprises 6,000 scanned objects in 190 daily categories, sharing common classes with popular 2D datasets (e.g., ImageNet and LVIS), benefiting the pursuit of generalizable 3D representations. 2) Rich Annotations: Each 3D object is captured with both 2D and 3D sensors, providing textured meshes, point clouds, multiview rendered images, and multiple real-captured videos. 3) Realistic Scans: The professional scanners support highquality object scans with precise shapes and realistic appearances. With the vast exploration space offered by OmniObject3D, we carefully set up four evaluation tracks: a) robust 3D perception, b) novel-view synthesis, c) neural surface reconstruction, and d) 3D object generation. Extensive studies are performed on these four benchmarks, revealing new observations, challenges, and opportunities for future research in realistic 3D vision.Comment: Project page: https://omniobject3d.github.io

Robust 3D Object Pose Estimation and Tracking from Monocular Images in Industrial Environments

Recent advances in Computer Vision are changing our way of living and enabling new applications for both leisure and professional use. Regrettably, in many industrial domains the spread of state-of-the-art technologies is made challenging by the abundance of nuisances that corrupt existing techniques beyond the required dependability. This is especially true for object localization and tracking, that is, the problem of detecting the presence of objects on images and videos and estimating their pose. This is a critical task for applications such as Augmented Reality (AR), robotic autonomous navigation, robotic object grasping, or production quality control; unfortunately, the reliability of existing techniques is harmed by visual features such as the abundance of specular and poorly textured objects, cluttered scenes, or artificial and in-homogeneous lighting. In this thesis, we propose two methods for robustly estimating the pose of a rigid object under the challenging conditions typical of industrial environments. Both methods rely on monocular images to handle metallic environments, on which depth cameras would fail; both are conceived with a limited computational and memory footprint, so that they are suitable for real-time applications such as AR. We test our methods on datasets issued from real user case scenarios, exhibiting challenging conditions. The first method is based on a global image alignment framework and a robust dense descriptor. Its global approach makes it robust in presence of local artifacts such as specularities appearing on metallic objects, ambiguous patterns like screws or wires, and poorly textured objects. Employing a global approach avoids the need of reliably detecting and matching local features across images, that become ill-conditioned tasks in the considered environments; on the other hand, current methods based on dense image alignment usually rely on luminous intensities for comparing the pixels, which is not robust in presence of challenging illumination artifacts. We show how the use of a dense descriptor computed as a non-linear function of luminous intensities, that we refer to as ``Descriptor Fields'', greatly enhances performances at a minimal computational overhead. Their low computational complexity and their ease of implementation make Descriptor Fields suitable for replacing intensities in a wide number of state-of-the-art techniques based on dense image alignment. Relying on a global approach is appropriate for overcoming local artifacts, but it can be un-effective when the target object undergoes extreme occlusions in cluttered environments. For this reason, we propose a second approach based on the detection of discriminative object parts. At the core of our approach is a novel representation for the 3D pose of the parts, that allows us to predict the 3D pose of the object even when only a single part is visible; when several parts are visible, we can easily combine them to compute a better pose of the object. The 3D pose we obtain is usually very accurate, even when only few parts are visible. We show how to use this representation in a robust 3D tracking framework. In addition to extensive comparisons with the state-of-the-art, we demonstrate our method on a practical Augmented Reality application for maintenance assistance in the ATLAS particle detector at CERN

TAP-Vid: A Benchmark for Tracking Any Point in a Video

Generic motion understanding from video involves not only tracking objects, but also perceiving how their surfaces deform and move. This information is useful to make inferences about 3D shape, physical properties and object interactions. While the problem of tracking arbitrary physical points on surfaces over longer video clips has received some attention, no dataset or benchmark for evaluation existed, until now. In this paper, we first formalize the problem, naming it tracking any point (TAP). We introduce a companion benchmark, TAP-Vid, which is composed of both real-world videos with accurate human annotations of point tracks, and synthetic videos with perfect ground-truth point tracks. Central to the construction of our benchmark is a novel semi-automatic crowdsourced pipeline which uses optical flow estimates to compensate for easier, short-term motion like camera shake, allowing annotators to focus on harder sections of video. We validate our pipeline on synthetic data and propose a simple end-to-end point tracking model TAP-Net, showing that it outperforms all prior methods on our benchmark when trained on synthetic data.Comment: Published in NeurIPS Datasets and Benchmarks track, 202

In-Field Estimation of Orange Number and Size by 3D Laser Scanning

The estimation of fruit load of an orchard prior to harvest is useful for planning harvest logistics and trading decisions. The manual fruit counting and the determination of the harvesting capacity of the field results are expensive and time-consuming. The automatic counting of fruits and their geometry characterization with 3D LiDAR models can be an interesting alternative. Field research has been conducted in the province of Cordoba (Southern Spain) on 24 ‘Salustiana’ variety orange trees—Citrus sinensis (L.) Osbeck—(12 were pruned and 12 unpruned). Harvest size and the number of each fruit were registered. Likewise, the unitary weight of the fruits and their diameter were determined (N = 160). The orange trees were also modelled with 3D LiDAR with colour capture for their subsequent segmentation and fruit detection by using a K-means algorithm. In the case of pruned trees, a significant regression was obtained between the real and modelled fruit number (R2 = 0.63, p = 0.01). The opposite case occurred in the unpruned ones (p = 0.18) due to a leaf occlusion problem. The mean diameters proportioned by the algorithm (72.15 ± 22.62 mm) did not present significant differences (p = 0.35) with the ones measured on fruits (72.68 ± 5.728 mm). Even though the use of 3D LiDAR scans is time-consuming, the harvest size estimation obtained in this research is very accurate

Real-time 3D hand reconstruction in challenging scenes from a single color or depth camera

Hands are one of the main enabling factors for performing complex tasks and humans naturally use them for interactions with their environment. Reconstruction and digitization of 3D hand motion opens up many possibilities for important applications. Hands gestures can be directly used for human–computer interaction, which is especially relevant for controlling augmented or virtual reality (AR/VR) devices where immersion is of utmost importance. In addition, 3D hand motion capture is a precondition for automatic sign-language translation, activity recognition, or teaching robots. Different approaches for 3D hand motion capture have been actively researched in the past. While being accurate, gloves and markers are intrusive and uncomfortable to wear. Hence, markerless hand reconstruction based on cameras is desirable. Multi-camera setups provide rich input, however, they are hard to calibrate and lack the flexibility for mobile use cases. Thus, the majority of more recent methods uses a single color or depth camera which, however, makes the problem harder due to more ambiguities in the input. For interaction purposes, users need continuous control and immediate feedback. This means the algorithms have to run in real time and be robust in uncontrolled scenes. These requirements, achieving 3D hand reconstruction in real time from a single camera in general scenes, make the problem significantly more challenging. While recent research has shown promising results, current state-of-the-art methods still have strong limitations. Most approaches only track the motion of a single hand in isolation and do not take background-clutter or interactions with arbitrary objects or the other hand into account. The few methods that can handle more general and natural scenarios run far from real time or use complex multi-camera setups. Such requirements make existing methods unusable for many aforementioned applications. This thesis pushes the state of the art for real-time 3D hand tracking and reconstruction in general scenes from a single RGB or depth camera. The presented approaches explore novel combinations of generative hand models, which have been used successfully in the computer vision and graphics community for decades, and powerful cutting-edge machine learning techniques, which have recently emerged with the advent of deep learning. In particular, this thesis proposes a novel method for hand tracking in the presence of strong occlusions and clutter, the first method for full global 3D hand tracking from in-the-wild RGB video, and a method for simultaneous pose and dense shape reconstruction of two interacting hands that, for the first time, combines a set of desirable properties previously unseen in the literature.Hände sind einer der Hauptfaktoren für die Ausführung komplexer Aufgaben, und Menschen verwenden sie auf natürliche Weise für Interaktionen mit ihrer Umgebung. Die Rekonstruktion und Digitalisierung der 3D-Handbewegung eröffnet viele Möglichkeiten für wichtige Anwendungen. Handgesten können direkt als Eingabe für die Mensch-Computer-Interaktion verwendet werden. Dies ist insbesondere für Geräte der erweiterten oder virtuellen Realität (AR / VR) relevant, bei denen die Immersion von größter Bedeutung ist. Darüber hinaus ist die Rekonstruktion der 3D Handbewegung eine Voraussetzung zur automatischen Übersetzung von Gebärdensprache, zur Aktivitätserkennung oder zum Unterrichten von Robotern. In der Vergangenheit wurden verschiedene Ansätze zur 3D-Handbewegungsrekonstruktion aktiv erforscht. Handschuhe und physische Markierungen sind zwar präzise, aber aufdringlich und unangenehm zu tragen. Daher ist eine markierungslose Handrekonstruktion auf der Basis von Kameras wünschenswert. Multi-Kamera-Setups bieten umfangreiche Eingabedaten, sind jedoch schwer zu kalibrieren und haben keine Flexibilität für mobile Anwendungsfälle. Daher verwenden die meisten neueren Methoden eine einzelne Farb- oder Tiefenkamera, was die Aufgabe jedoch schwerer macht, da mehr Ambiguitäten in den Eingabedaten vorhanden sind. Für Interaktionszwecke benötigen Benutzer kontinuierliche Kontrolle und sofortiges Feedback. Dies bedeutet, dass die Algorithmen in Echtzeit ausgeführt werden müssen und robust in unkontrollierten Szenen sein müssen. Diese Anforderungen, 3D-Handrekonstruktion in Echtzeit mit einer einzigen Kamera in allgemeinen Szenen, machen das Problem erheblich schwieriger. Während neuere Forschungsarbeiten vielversprechende Ergebnisse gezeigt haben, weisen aktuelle Methoden immer noch Einschränkungen auf. Die meisten Ansätze verfolgen die Bewegung einer einzelnen Hand nur isoliert und berücksichtigen keine alltäglichen Umgebungen oder Interaktionen mit beliebigen Objekten oder der anderen Hand. Die wenigen Methoden, die allgemeinere und natürlichere Szenarien verarbeiten können, laufen nicht in Echtzeit oder verwenden komplexe Multi-Kamera-Setups. Solche Anforderungen machen bestehende Verfahren für viele der oben genannten Anwendungen unbrauchbar. Diese Dissertation erweitert den Stand der Technik für die Echtzeit-3D-Handverfolgung und -Rekonstruktion in allgemeinen Szenen mit einer einzelnen RGB- oder Tiefenkamera. Die vorgestellten Algorithmen erforschen neue Kombinationen aus generativen Handmodellen, die seit Jahrzehnten erfolgreich in den Bereichen Computer Vision und Grafik eingesetzt werden, und leistungsfähigen innovativen Techniken des maschinellen Lernens, die vor kurzem mit dem Aufkommen neuronaler Netzwerke entstanden sind. In dieser Arbeit werden insbesondere vorgeschlagen: eine neuartige Methode zur Handbewegungsrekonstruktion bei starken Verdeckungen und in unkontrollierten Szenen, die erste Methode zur Rekonstruktion der globalen 3D Handbewegung aus RGB-Videos in freier Wildbahn und die erste Methode zur gleichzeitigen Rekonstruktion von Handpose und -form zweier interagierender Hände, die eine Reihe wünschenwerter Eigenschaften komibiniert

Food Recognition and Volume Estimation in a Dietary Assessment System

Recently obesity has become an epidemic and one of the most serious worldwide public health concerns of the 21st century. Obesity diminishes the average life expectancy and there is now convincing evidence that poor diet, in combination with physical inactivity are key determinants of an individual s risk of developing chronic diseases such as cancer, cardiovascular disease or diabetes. Assessing what people eat is fundamental to establishing the link between diet and disease. Food records are considered the best approach for assessing energy intake. However, this method requires literate and highly motivated subjects. This is a particular problem for adolescents and young adults who are the least likely to undertake food records. The ready access of the majority of the population to mobile phones (with integrated camera, improved memory capacity, network connectivity and faster processing capability) has opened up new opportunities for dietary assessment. The dietary information extracted from dietary assessment provide valuable insights into the cause of diseases that greatly helps practicing dietitians and researchers to develop subsequent approaches for mounting intervention programs for prevention. In such systems, the camera in the mobile phone is used for capturing images of food consumed and these images are then processed to automatically estimate the nutritional content of the food. However, food objects are deformable objects that exhibit variations in appearance, shape, texture and color so the food classification and volume estimation in these systems suffer from lower accuracy. The improvement of the food recognition accuracy and volume estimation accuracy are challenging tasks. This thesis presents new techniques for food classification and food volume estimation. For food recognition, emphasis was given to texture features. The existing food recognition techniques assume that the food images will be viewed at similar scales and from the same viewpoints. However, this assumption fails in practical applications, because it is difficult to ensure that a user in a dietary assessment system will put his/her camera at the same scale and orientation to capture food images as that of the target food images in the database. A new scale and rotation invariant feature generation approach that applies Gabor filter banks is proposed. To obtain scale and rotation invariance, the proposed approach identifies the dominant orientation of the filtered coefficient and applies a circular shifting operation to place this value at the first scale of dominant direction. The advantages of this technique are it does not require the scale factor to be known in advance and it is scale/and rotation invariant separately and concurrently. This approach is modified to achieve improved accuracy by applying a Gaussian window along the scale dimension which reduces the impact of high and low frequencies of the filter outputs enabling better matching between the same classes. Besides automatic classification, semi automatic classification and group classification are also considered to have an idea about the improvement. To estimate the volume of a food item, a stereo pair is used to recover the structure as a 3D point cloud. A slice based volume estimation approach is proposed that converts the 3D point cloud to a series of 2D slices. The proposed approach eliminates the problem of knowing the distance between two cameras with the help of disparities and depth information from a fiducial marker. The experimental results show that the proposed approach can provide an accurate estimate of food volume