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Recent advancements in 3D generation have garnered considerable interest due to their potential applications. Despite these advancements, the field faces persistent challenges in multi-conditional control, primarily due to the lack of paired datasets and ...

Diffusion-based 3D generative models have seen significant progress recently. However, their further advancement is limited by issues like mode collapse and slow generation speed. In this paper, we present a coarse-to-fine 3D Gaussian generation ...

The current field of 3D generation has made significant progress, yet achieving high-fidelity 3D object reconstruction from a single-view image remains a challenging task. However, we find that recent StyleGAN-based 3D GANs are primarily used for ...

Traditional neural radiance fields for rendering novel views require intensive input images and pre-scene optimization, which limits their practical applications. We propose a generalization method to infer scenes from input images and perform ...

Although past learning-based Multi-View Stereo methods performed well, they still struggle to reconstruct regions with occlusions or weak textures. In this paper, we propose a Multi-View Stereo Net using attention mechanism and multi-source cost ...

We propose MVD-NeRF, a method to recover high-fidelity mesh from neural radiance fields(NeRFs). The phenomenon of shape radiance ambiguity, where the radiance of a point changes significantly when viewed from different angles, leads to incorrect ...

The Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene using a multilayer perceptron (MLP) combined with classic volume rendering and uses positional encoding techniques to increase image resolution. ...

In this paper, we investigate computation offloading in a multi-access wireless network, which supports both cellular and WiFi connectivity between a mobile user (MU) and the edge server. The MU decides to process an arrived computation task locally at ...

Mobile edge computing (MEC) is a promising paradigm for alleviating the computation burden of resource-constrained mobile devices. Nevertheless, the majority of existing efforts concentrate on offloading computations from mobile de-vices to an edge ...

The images are often compressed to reduce storage usage or accelerate image transmission. However, the compression process always results in the loss of image details, such as edge details, which degrades the visual experience. Plenty of reconstruction ...

Traditional Wi-Fi based action recognition models often utilize only amplitude information from CSI, with phase information simply discarded due to phase error problems. Such a design decision inevitably limits the information utilization, thus ...

Deep neural networks (DNNs) have been extensively studied in medical image segmentation. However, existing DNNs often need to train shape models for each object to be segmented, which may yield results that violate cardiac anatomical structure ...

Segmentation of the left ventricle from cardiac magnetic resonance images provides important supplementary information for the diagnosis and treatment follow-up of cardiovascular diseases, the main cause of deaths worldwide. In this paper, we propose a ...

Visual crowdsensing (VCS) uses built-in cameras of smart devices and asks people to capture the details of interesting objects or views in the real world. To decrease the incentive costs, we propose the task allocation method to assign light subtasks of ...

Cloud system is difficult to be modeled and evaluated due to its large scale, complex structures, outstanding dynamics, and strong correlations among layers. Aiming to solve this problem, an evaluation model of cloud system is proposed based on queuing ...

Wireless sensor networks WSNs are often deployed in hostile environments, thus being subjected to great security risks. However, due to the influence of environment and dynamic topology, the communication radiuses of all nodes are not strictly consistent,...

Map Reduce has emerged as a popular computing model for parallel processing of cloud computing. Map Reduce performance analysis and modeling is needed to guide performance optimization and job scheduling. However, we observed that it is difficult to ...

We study online adaptive scheduling for multiple sets of parallel jobs, where each set may contain one or more jobs with time-varying parallelism. This two-level scheduling scenario arises naturally when multiple parallel applications are submitted by ...

Both fairness and efficiency are crucial measures for the performance of parallel applications on multiprocessor systems. In this paper, we study online adaptive scheduling for multiple sets of such applications, where each set may contain one or more ...

The proliferation of multi-core architectures has led to explosive development of parallel applications using programming models, such as OpenMP, TBB, and Cilk, etc. With increasing number of cores, however, it becomes harder to efficiently schedule ...