201 research outputs found

    Ultra-low-velocity anomaly inside the Pacific Slab near the 410-km discontinuity

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    The upper boundary of the mantle transition zone, known as the “410-km discontinuity”, is attributed to the phase transformation of the mineral olivine (α) to wadsleyite (β olivine). Here we present observations of triplicated P-waves from dense seismic arrays that constrain the structure of the subducting Pacific slab near the 410-km discontinuity beneath the northern Sea of Japan. Our analysis of P-wave travel times and waveforms at periods as short as 2 s indicates the presence of an ultra-low-velocity layer within the cold slab, with a P-wave velocity that is at least ≈20% lower than in the ambient mantle and an apparent thickness of ≈20 km along the wave path. This ultra-low-velocity layer could contain unstable material (e.g., poirierite) with reduced grain size where diffusionless transformations are favored

    Voxurf: Voxel-based Efficient and Accurate Neural Surface Reconstruction

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    Neural surface reconstruction aims to reconstruct accurate 3D surfaces based on multi-view images. Previous methods based on neural volume rendering mostly train a fully implicit model with MLPs, which typically require hours of training for a single scene. Recent efforts explore the explicit volumetric representation to accelerate the optimization via memorizing significant information with learnable voxel grids. However, existing voxel-based methods often struggle in reconstructing fine-grained geometry, even when combined with an SDF-based volume rendering scheme. We reveal that this is because 1) the voxel grids tend to break the color-geometry dependency that facilitates fine-geometry learning, and 2) the under-constrained voxel grids lack spatial coherence and are vulnerable to local minima. In this work, we present Voxurf, a voxel-based surface reconstruction approach that is both efficient and accurate. Voxurf addresses the aforementioned issues via several key designs, including 1) a two-stage training procedure that attains a coherent coarse shape and recovers fine details successively, 2) a dual color network that maintains color-geometry dependency, and 3) a hierarchical geometry feature to encourage information propagation across voxels. Extensive experiments show that Voxurf achieves high efficiency and high quality at the same time. On the DTU benchmark, Voxurf achieves higher reconstruction quality with a 20x training speedup compared to previous fully implicit methods

    HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image

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    3D content creation from a single image is a long-standing yet highly desirable task. Recent advances introduce 2D diffusion priors, yielding reasonable results. However, existing methods are not hyper-realistic enough for post-generation usage, as users cannot view, render and edit the resulting 3D content from a full range. To address these challenges, we introduce HyperDreamer with several key designs and appealing properties: 1) Viewable: 360 degree mesh modeling with high-resolution textures enables the creation of visually compelling 3D models from a full range of observation points. 2) Renderable: Fine-grained semantic segmentation and data-driven priors are incorporated as guidance to learn reasonable albedo, roughness, and specular properties of the materials, enabling semantic-aware arbitrary material estimation. 3) Editable: For a generated model or their own data, users can interactively select any region via a few clicks and efficiently edit the texture with text-based guidance. Extensive experiments demonstrate the effectiveness of HyperDreamer in modeling region-aware materials with high-resolution textures and enabling user-friendly editing. We believe that HyperDreamer holds promise for advancing 3D content creation and finding applications in various domains.Comment: SIGGRAPH Asia 2023 (conference track). Project page: https://ys-imtech.github.io/HyperDreamer

    GPT4Point: A Unified Framework for Point-Language Understanding and Generation

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    Multimodal Large Language Models (MLLMs) have excelled in 2D image-text comprehension and image generation, but their understanding of the 3D world is notably deficient, limiting progress in 3D language understanding and generation. To solve this problem, we introduce GPT4Point, an innovative groundbreaking point-language multimodal model designed specifically for unified 3D object understanding and generation within the MLLM framework. GPT4Point as a powerful 3D MLLM seamlessly can execute a variety of point-text reference tasks such as point-cloud captioning and Q&A. Additionally, GPT4Point is equipped with advanced capabilities for controllable 3D generation, it can get high-quality results through a low-quality point-text feature maintaining the geometric shapes and colors. To support the expansive needs of 3D object-text pairs, we develop Pyramid-XL, a point-language dataset annotation engine. It constructs a large-scale database over 1M objects of varied text granularity levels from the Objaverse-XL dataset, essential for training GPT4Point. A comprehensive benchmark has been proposed to evaluate 3D point-language understanding capabilities. In extensive evaluations, GPT4Point has demonstrated superior performance in understanding and generation

    Frequency-diverse MIMO metasurface antenna for computational imaging with aperture rotation technique

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    Metasurface antennas have been proposed for computational imaging (CI) systems, which can reconstruct images without using mechanical scanning or large antenna arrays. In a CI system based on metasurface antennas, a variety of different radiation fields, which can be applied to sample the objects, are generated by exciting different frequency points in broadband. According to the compressed sensing theory, the imaging performance of the system is mainly limited by frequency-diversity radiation modes. In general, it is difficult to achieve rich radiation modes; therefore, a special design of metasurface aperture is required. In this paper, we propose a frequency-diversity MIMO metasurface antenna that consists of 2 × 2 sub-apertures with randomly distributed surface impedance. By employing the aperture rotation technique (ART) which rotates the MIMO metasurface antenna around the panel axis, the pseudo-randomness of the radiation fields is utilized. The diversity of the radiation field is improved on the premise of ensuring the relatively low complexity of the system. The ART significantly improves the measurement richness at the cost of increasing the measurement time. The performance of the proposed method is evaluated through simulations and experiments, suggesting that the proposed 2 × 2 MIMO metasurface antenna and the ART are effective to reconstruct high-quality images

    The evaluation of government subsidy policies on carbon emissions in the port collection and distribution network: a case study of Guangzhou Port

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    The collection and distribution network of ports is the main cause of carbon emissions. The carbon peak is a basic policy in China, and the subsidy policy is one of the common measures used by the government to incentivize carbon reduction. We analyzed the transportation methods and the flow direction of a port and proposed a carbon emission calculation method based on emission factors. Based on the transportation time and the cost, a generalized transportation utility function was constructed, and the logit model was used to analyze the impacts of subsidy policies on transportation, thus calculating the effects of the subsidies on carbon reduction. We used Guangzhou Port as a case study, and calculated the carbon reduction effects in six different subsidy policy scenarios and concluded that the absolute carbon reduction value was proportional to the subsidy intensity. In addition, we constructed a subsidy carbon reduction efficiency index and found that the Guangzhou Port collection and distribution network had higher subsidy carbon reduction efficiency in low-subsidy scenarios. Finally, a sensitivity analysis was conducted on the subsidy parameters, and scenario 8 was found to have the highest subsidy carbon reduction efficiency. This achievement can provide decision support for the carbon emission strategy of the port collection and distribution network

    An Incremental Unified Framework for Small Defect Inspection

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    Artificial Intelligence (AI)-driven defect inspection is pivotal in industrial manufacturing. Yet, many methods, tailored to specific pipelines, grapple with diverse product portfolios and evolving processes. Addressing this, we present the Incremental Unified Framework (IUF), which can reduce the feature conflict problem when continuously integrating new objects in the pipeline, making it advantageous in object-incremental learning scenarios. Employing a state-of-the-art transformer, we introduce Object-Aware Self-Attention (OASA) to delineate distinct semantic boundaries. Semantic Compression Loss (SCL) is integrated to optimize non-primary semantic space, enhancing network adaptability for novel objects. Additionally, we prioritize retaining the features of established objects during weight updates. Demonstrating prowess in both image and pixel-level defect inspection, our approach achieves state-of-the-art performance, proving indispensable for dynamic and scalable industrial inspections. Our code will be released at \url{https://github.com/jqtangust/IUF}

    Deep Geophysical Anomalies Beneath the Changbaishan Volcano

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    Subsurface imaging is key to understanding the origin of intraplate volcanoes. The Changbaishan volcano, located about 2,000 km away from the western Pacific subduction zone, has several debated origins. To investigate this, we compared regional seismic tomography with the electrical resistivity results and obtained high-resolution 1D and quasi-2D velocity-depth profiles. We show that the upper mantle is characterized by two anomalies exhibiting distinct features which cannot be explained by the same mechanism. We document a localized low-velocity anomaly atop the 410-km discontinuity, where the P-wave velocity is reduced more than that of the S-wave (i.e., lower Vp/Vs). We propose that this anomaly is caused by the reduction of the effective moduli during the phase transformation of olivine. The other anomaly, located between 300 and 370 km depth, reveals a significant reduction of the S-wave velocity (i.e., higher Vp/Vs), associated with a reduction of the electrical resistivity, altogether consistent with partial melting
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