CD2^2: Fine-grained 3D Mesh Reconstruction with Twice Chamfer Distance

Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and most of meshes have two severe problems Vertices Clustering (VC) and Illegal Twist (IT). By diving into the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is the root causes of VC and IT problems in the training of deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates well-structured meshes and outperforms others by alleviating VC and IT problems.Comment: under major review in TOM

Retrieval and classification methods for textured 3D models: a comparative study

International audienceThis paper presents a comparative study of six methods for the retrieval and classification of tex-tured 3D models, which have been selected as representative of the state of the art. To better analyse and control how methods deal with specific classes of geometric and texture deformations, we built a collection of 572 synthetic textured mesh models, in which each class includes multiple texture and geometric modifications of a small set of null models. Results show a challenging, yet lively, scenario and also reveal interesting insights in how to deal with texture information according to different approaches, possibly working in the CIELab as well as in modifications of the RGB colour space

Detail Enhancing Denoising of Digitized 3D Models from a Mobile Scanning System

The acquisition process of digitizing a large-scale environment produces an enormous amount of raw geometry data. This data is corrupted by system noise, which leads to 3D surfaces that are not smooth and details that are distorted. Any scanning system has noise associate with the scanning hardware, both digital quantization errors and measurement inaccuracies, but a mobile scanning system has additional system noise introduced by the pose estimation of the hardware during data acquisition. The combined system noise generates data that is not handled well by existing noise reduction and smoothing techniques. This research is focused on enhancing the 3D models acquired by mobile scanning systems used to digitize large-scale environments. These digitization systems combine a variety of sensors – including laser range scanners, video cameras, and pose estimation hardware – on a mobile platform for the quick acquisition of 3D models of real world environments. The data acquired by such systems are extremely noisy, often with significant details being on the same order of magnitude as the system noise. By utilizing a unique 3D signal analysis tool, a denoising algorithm was developed that identifies regions of detail and enhances their geometry, while removing the effects of noise on the overall model. The developed algorithm can be useful for a variety of digitized 3D models, not just those involving mobile scanning systems. The challenges faced in this study were the automatic processing needs of the enhancement algorithm, and the need to fill a hole in the area of 3D model analysis in order to reduce the effect of system noise on the 3D models. In this context, our main contributions are the automation and integration of a data enhancement method not well known to the computer vision community, and the development of a novel 3D signal decomposition and analysis tool. The new technologies featured in this document are intuitive extensions of existing methods to new dimensionality and applications. The totality of the research has been applied towards detail enhancing denoising of scanned data from a mobile range scanning system, and results from both synthetic and real models are presented

Design and manufacturing of advanced composite aircraft structures using automated tow placement

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1996.Includes bibliographical references (leaves 89-91).by Ian B. Land.S.M

Investigation Of Predicted Helicopter Rotorhub Drag and Wake Flow with Reduced Order Modeling

The rotor hub is one of the most important components of the modern helicopter. This complex collection of linkages and plates has numerous responsibilities, including the translation of pilot input to system response, anchoring the blades to the rotor mast, and sustaining the various forces transmitted by the blades. Due its intricate design and relatively small sized components the rotor hub interacts with the incoming flow to create a highly chaotic, turbulent wake which impinges on the fuselage and empennage. This assembly has also been found to be one of the primary contributors to the total vehicle parasite drag. Unfortunately studying the rotor hub and its wake more closely is made difficult by the limitation of both modern experimental and computational methods. From an experimental standpoint tests are expensive to run, difficult to gather large amounts of data from, and can require full or high scale Reynolds numbers. Computational Fluid Dynamics (CFD) predictions of hub flows are limited by high grid resolution requirements, and lengthy grid generation and simulation times. Modal decompositions provide robust options for reduced order modeling of fluid flows. Several modal decomposition methods are tested for the validity of their application to the complex flow fields that form around rotor hubs. Four variations of two rotor hub designs, a baseline and low drag, are simulated in forward flight. This selection of hubs was chose to examine the effects of both hub geometry and aerodynamic optimization on the rotor hub surface forces and wake. Flow solutions were found using the OVERFLOW2.2n overset, structured, RANS solver created and maintained by NASA. Simulations were conducted using a fully turbulent model and the grid generation and computational equations specifics are discussed in further detail. Each of the four hub variants was subjected to the same flow conditions. Several variants of modal decomposition and other post processing techniques were used on the resultant surface force and wake data in order to Characterize the hub flow field

Modeling Surfaces from Volume Data Using Nonparallel Contours

Magnetic resonance imaging: MRI) and computed tomography: CT) scanners have long been used to produce three-dimensional samplings of anatomy elements for use in medical visualization and analysis. From such datasets, physicians often need to construct surfaces representing anatomical shapes in order to conduct treatment, such as irradiating a tumor. Traditionally, this is done through a time-consuming and error-prone process in which an experienced scientist or physician marks a series of parallel contours that outline the structures of interest. Recent advances in surface reconstruction algorithms have led to methods for reconstructing surfaces from nonparallel contours that could greatly reduce the manual component of this process. Despite these technological advances, the segmentation process has remained unchanged. This dissertation takes the first steps toward bridging the gap between the new surface reconstruction technologies and bringing those methods to use in clinical practice. We develop VolumeViewer, a novel interface for modeling surfaces from volume data by allowing the user to sketch contours on arbitrarily oriented cross-sections of the volume. We design the algorithms necessary to support nonparallel contouring, and we evaluate the system with medical professionals using actual patient data. In this way, we begin to understand how nonparallel contouring can aid the segmentation process and expose the challenges associated with a nonparallel contouring system in practice

Energy efficient transport technology: Program summary and bibliography

The Energy Efficient Transport (EET) Program began in 1976 as an element of the NASA Aircraft Energy Efficiency (ACEE) Program. The EET Program and the results of various applications of advanced aerodynamics and active controls technology (ACT) as applicable to future subsonic transport aircraft are discussed. Advanced aerodynamics research areas included high aspect ratio supercritical wings, winglets, advanced high lift devices, natural laminar flow airfoils, hybrid laminar flow control, nacelle aerodynamic and inertial loads, propulsion/airframe integration (e.g., long duct nacelles) and wing and empennage surface coatings. In depth analytical/trade studies, numerous wind tunnel tests, and several flight tests were conducted. Improved computational methodology was also developed. The active control functions considered were maneuver load control, gust load alleviation, flutter mode control, angle of attack limiting, and pitch augmented stability. Current and advanced active control laws were synthesized and alternative control system architectures were developed and analyzed. Integrated application and fly by wire implementation of the active control functions were design requirements in one major subprogram. Additional EET research included interdisciplinary technology applications, integrated energy management, handling qualities investigations, reliability calculations, and economic evaluations related to fuel savings and cost of ownership of the selected improvements

AutoHair: Fully Automatic Hair Modeling from A Single Image

We introduce AutoHair, the first fully automatic method for 3D hair modeling from a single portrait image, with no user interaction or parameter tuning. Our method efficiently generates complete and high-quality hair geometries, which are comparable to those generated by the state-of-the-art methods, where user interaction is required. The core components of our method are: a novel hierarchical deep neural network for automatic hair segmentation and hair growth direction estimation, trained over an annotated hair image database; and an efficient and automatic data-driven hair matching and modeling algorithm, based on a large set of 3D hair exemplars. We demonstrate the efficacy and robustness of our method on Internet photos, resulting in a database of around 50K 3D hair models and a corresponding hairstyle space that covers a wide variety of real-world hairstyles. We also show novel applications enabled by our method, including 3D hairstyle space navigation and hair-aware image retrieval

Aeronautical Engineering: A Continuing Bibliography with indexes

This bibliography lists 426 reports, articles and other documents introduced into the NASA scientific and technical information system in August 1984. Reports are cited in the area of Aeronautical Engineering. The coverage includes documents on the engineering and theoretical aspects of design, construction, evaluation, testing operation and performance of aircraft (including aircraft engines) and associated components, equipment and systems