ZeroMesh: Zero-shot Single-view 3D Mesh Reconstruction

Single-view 3D object reconstruction is a fundamental and challenging computer vision task that aims at recovering 3D shapes from single-view RGB images. Most existing deep learning based reconstruction methods are trained and evaluated on the same categories, and they cannot work well when handling objects from novel categories that are not seen during training. Focusing on this issue, this paper tackles Zero-shot Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories and encourage models to reconstruct objects literally. Specifically, we propose an end-to-end two-stage network, ZeroMesh, to break the category boundaries in reconstruction. Firstly, we factorize the complicated image-to-mesh mapping into two simpler mappings, i.e., image-to-point mapping and point-to-mesh mapping, while the latter is mainly a geometric problem and less dependent on object categories. Secondly, we devise a local feature sampling strategy in 2D and 3D feature spaces to capture the local geometry shared across objects to enhance model generalization. Thirdly, apart from the traditional point-to-point supervision, we introduce a multi-view silhouette loss to supervise the surface generation process, which provides additional regularization and further relieves the overfitting problem. The experimental results show that our method significantly outperforms the existing works on the ShapeNet and Pix3D under different scenarios and various metrics, especially for novel objects

Division schemes under uncertainty of claims

summary:In some economic or social division problems, we may encounter uncertainty of claims, that is, a certain amount of estate has to be divided among some claimants who have individual claims on the estate, and the corresponding claim of each claimant can vary within a closed interval or fuzzy interval. In this paper, we classify the division problems under uncertainty of claims into three subclasses and present several division schemes from the perspective of axiomatizations, which are consistent with the classical bankruptcy rules in particular cases. When claims of claimants have fuzzy interval uncertainty, we settle such type of division problems by turning them into division problems under interval uncertainty

Forces inside a strongly-coupled scalar nucleon

We investigate the gravitational form factors of a strongly coupled scalar theory that mimic the interaction between the nucleon and the pion. The non-perturbative calculation is based on the light-front Hamiltonian formalism. We renormalize the energy-momentum tensor with a Fock sector dependent scheme. We also systematically analyze the Lorentz structure of the energy-momentum tensor and identify the suitable hadron matrix elements to extract the form factors, avoiding the contamination of spurious contributions. We verify that the extracted form factors obey momentum conservation as well as the mechanical stability condition. From the gravitational form factors, we compute the energy and pressure distributions of the system. Furthermore, we show that utilizing the Hamiltonian eigenvalue equation, the off-diagonal Fock sector contributions from the interaction term can be converted to diagonal Fock sector contributions, yielding a systematic non-perturbative light-front wave function representation of the energies and forces inside the system.Comment: 30 pages, 21 figure

Neural Vector Fields: Generalizing Distance Vector Fields by Codebooks and Zero-Curl Regularization

Recent neural networks based surface reconstruction can be roughly divided into two categories, one warping templates explicitly and the other representing 3D surfaces implicitly. To enjoy the advantages of both, we propose a novel 3D representation, Neural Vector Fields (NVF), which adopts the explicit learning process to manipulate meshes and implicit unsigned distance function (UDF) representation to break the barriers in resolution and topology. This is achieved by directly predicting the displacements from surface queries and modeling shapes as Vector Fields, rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods do. In this way, our approach is capable of encoding both the distance and the direction fields so that the calculation of direction fields is differentiation-free, circumventing the non-trivial surface extraction step. Furthermore, building upon NVFs, we propose to incorporate two types of shape codebooks, \ie, NVFs (Lite or Ultra), to promote cross-category reconstruction through encoding cross-object priors. Moreover, we propose a new regularization based on analyzing the zero-curl property of NVFs, and implement this through the fully differentiable framework of our NVF (ultra). We evaluate both NVFs on four surface reconstruction scenarios, including watertight vs non-watertight shapes, category-agnostic reconstruction vs category-unseen reconstruction, category-specific, and cross-domain reconstruction

Neural Vector Fields: Implicit Representation by Explicit Learning

Deep neural networks (DNNs) are widely applied for nowadays 3D surface reconstruction tasks and such methods can be further divided into two categories, which respectively warp templates explicitly by moving vertices or represent 3D surfaces implicitly as signed or unsigned distance functions. Taking advantage of both advanced explicit learning process and powerful representation ability of implicit functions, we propose a novel 3D representation method, Neural Vector Fields (NVF). It not only adopts the explicit learning process to manipulate meshes directly, but also leverages the implicit representation of unsigned distance functions (UDFs) to break the barriers in resolution and topology. Specifically, our method first predicts the displacements from queries towards the surface and models the shapes as \textit{Vector Fields}. Rather than relying on network differentiation to obtain direction fields as most existing UDF-based methods, the produced vector fields encode the distance and direction fields both and mitigate the ambiguity at "ridge" points, such that the calculation of direction fields is straightforward and differentiation-free. The differentiation-free characteristic enables us to further learn a shape codebook via Vector Quantization, which encodes the cross-object priors, accelerates the training procedure, and boosts model generalization on cross-category reconstruction. The extensive experiments on surface reconstruction benchmarks indicate that our method outperforms those state-of-the-art methods in different evaluation scenarios including watertight vs non-watertight shapes, category-specific vs category-agnostic reconstruction, category-unseen reconstruction, and cross-domain reconstruction. Our code is released at https://github.com/Wi-sc/NVF.Comment: Accepted by CVPR2023. Video: https://www.youtube.com/watch?v=GMXKoJfmHr

Ultrasonic Tomography of Immersion Circular Array by Hyperbola Algorithm

This paper presents a development and research of a non-invasive ultrasonic tomography for imaging gas/liquid two-phase flow. Ultrasonic transmitting and receiving are implemented using a circular array model that consists of 36 transducers. COMSOL Multiphysics® software is adopted for the simulation of the ultrasonic propagation in the detecting zone. Various two-phase flows with different gas distributions are radiated by ultrasonic waves and the reflection mode approach is utilized for detecting the scattering waves after the generation of fan-shaped beam. Ultrasonic attenuation and sound speed are both taken into consideration while reconstructing the two-phase flow images under the inhomogeneous medium conditions. The inversion procedure of the image reconstruction is realized using the hyperbola algorithm, which in return demonstrates the feasibility and validity of the proposed circular array model

Fabrication of highly hydrophobic two-component thermosetting polyurethane surfaces with silica nanoparticles

Highly hydrophobic thermosetting polyurethane (TSU) surfaces with micro-nano hierarchical structures were developed by a simple process combined with sandpaper templates and nano-silica embellishment. Sandpapers with grit sizes varying from 240 to 7000 grit were used to obtain micro-scale roughness on an intrinsic hydrophilic TSU surface. The surface wettability was investigated by contact angle measurement. It was found that the largest contact angle of the TSU surface without nanoparticles at 102 ± 3 ° was obtained when the template was 240-grit sandpaper and the molding progress started after 45 min curing of TSU. Silica nanoparticles modified with polydimethylsiloxane were scattered onto the surfaces of both the polymer and the template to construct the desirable nanostructures. The influences of the morphology, surface composition and the silica content on the TSU surface wettability were studied by scanning electron microscopy (SEM), attenuated total reflection (ATR) infrared (IR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and contact angle measurements. The surface of the TSU/SiO2 nanocomposites containing 4 wt% silica nanoparticles exhibited a distinctive dual-scale structure and excellent hydrophobicity with the contact angle above 150°. The mechanism of wettability was also discussed by Wenzel model and Cassie-Baxter model