Implicit surface representations have emerged as a powerful tool for the task of 3D reconstruction due to their excellent performance. Yet, when the normal information cannot be available, the previous methods often lead to unsatisfactory reconstruction results, even failure. To this end, we propose a winding number—guided implicit surface reconstruction method, which mainly consists of a winding number—guided regularizer and a dynamic edge sampling strategy. Among them, the winding number-guided regularizer can effectively constrain the global normal consistency of the input raw data, as well as improve the unsatisfactory implicit surface reconstruction result caused by the unavailability of normal information. Meanwhile, in order to reduce the excessive smoothing at sharp edges of implicit surface, we proposed a dynamic edge sampling strategy for sampling near the sharp edge regions of 3D shape, which can effectively avoid the regularizer from smoothing all regions. Finally, we combine them with a simple data term for robust implicit surface reconstruction. Compared with the state-of-the-art methods, experimental results show that our method significantly improves the quality of 3D reconstruction results. In addition, since the winding number-guided regularizer effectively constraints the globally consistent normal of the input 3D raw data, our method can also receive an additional gift, namely the globally consistent normal estimation results of 3D raw data.

Three-dimensional (3D) cloud modeling plays a pivotal role in advancing atmospheric models and enhancing natural phenomena visualization systems. Nevertheless, the high-quality reconstruction of clouds remains a significant challenge, primarily due to their inherent heterogeneous nature as volumetric media. Image-based modeling approaches offer a promising solution to this challenge. This paper presents a novel two-stage neural network architecture for 3D cloud reconstruction from single image. The first stage introduces an innovative view synthesis network built upon Stable Diffusion, incorporating two specialized modules: a cloud mapper and a viewpoint mapper, which collaboratively generate novel perspective views from a single input image. The second stage implements a physics-based differentiable rendering framework to construct a 3D cloud reconstruction network, leveraging the synthesized multi-view images to optimize a volumetric density grid representation. To enhance the reconstruction fidelity, we integrate real-world cloud density distribution statistics and implement a post-processing refinement using Perlin-Worley noise combined with Fractal Brownian Motion (FBM) for erosion effects. Additionally, to mitigate the inherent limitations of geometric information extraction from single-view images, we developed a comprehensive cloud simulation dataset for pre-training the viewpoint mapper module. This dataset encompasses multi-view cloud images with corresponding camera extrinsic parameters, capturing a diverse range of cloud formations. Extensive quantitative evaluations and qualitative assessments demonstrate the efficacy and potential of our proposed two-stage network in achieving accurate 3D cloud reconstruction from single-view images.

Reconstructing surfaces from diverse raw data in computer graphics poses an enduring challenge. While recent methods deploy neural networks for direct implicit surface reconstruction, they struggle with degraded raw data quality, especially in edge regions. To address this, we advocate for employing high-order total generalized variation (TGV) as a regularization term for implicit surface representation. Acknowledging the non-trivial nature of extending typical image processing methods to implicit surfaces, we present an end-to-end trainable network framework for TGV in implicit surface reconstruction. This approach preserves sharp features, enhances smooth region recovery, and minimizes artificial artifacts. Simultaneously, we address the challenge of increased computational complexity associated with current algorithms by predicting it directly through an implicit neural function. Experimental results demonstrate the efficacy of our technical approach, providing a promising solution for robust implicit surface reconstruction.

Knowledge graph (KG) has been used to provide additional relational knowledge about stance targets, thereby enhancing stance detection performance. However, the utilization efficiency of KG remains unconcerned and we find that the knowledge adoption amount is low in practice. In this work, we propose an efficient framework to improve the exploitation of KG, in which we integrate three different matching mechanisms to improve knowledge usage. A knowledge injection module is also introduced to explore enriched knowledge by constructing a supplementary set. These supplements contribute to stance detection through a text-pair classification mechanism. The experimental results on different datasets demonstrate that our approach outperforms existing works on knowledge graph exploitation and stance detection. Our code is available at https://github.com/kxy-cheng/EMKG.

Great progress has been made in rendering translucent materials in recent years, but automatically estimating parameters for heterogeneous materials such as jade and human skin remains a challenging task, often requiring specialized and expensive physical measurement devices. In this paper, we present a novel approach for estimating and transferring the parameters of heterogeneous translucent materials from a single 2D image to 3D models. Our method consists of four key steps: (1) An efficient viewpoint selection algorithm to minimize redundancy and ensure comprehensive coverage of the model. (2) Initializing a homogeneous translucent material to render initial images for translucent dataset. (3) Edit the rendered translucent images to update the translucent dataset. (4) Optimize the edited translucent results onto material parameters using inverse rendering techniques. Our approach offers a practical and accessible solution that overcomes the limitations of existing methods, which often rely on complex and costly specialized devices. We demonstrate the effectiveness and superiority of our proposed method through extensive experiments, showcasing its ability to transfer and edit high-quality heterogeneous translucent materials on 3D models, surpassing the results achieved by previous techniques in 3D scene editing.