Image Reconstruction Dataset, Compared to previous dataset, our dataset has the following advantages: (1) GenLCA is a diffusion-based generative model for creating photorealistic, full-body 3D avatars using millions of in-the-wild video frames. Binary- contrast, 10 × 10-patch images (2^100 possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred We propose a comprehensive dataset for the purpose of CT reconstruction. A comprehensive evaluation of the pre-trained DUSt3R/MASt3R/VGGT models on the aerial blocks of the UseGeo dataset for pose estimation and dense 3D reconstruction suggests An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis Contact: Jürgen Sturm We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the Variational Autoencoders (VAEs) are generative models that learn a smooth, probabilistic latent space, allowing them not only to compress and This work introduces a hybrid CNN–LSTM-based framework to address the sparsity problem in light fields by generating novel camera poses and the corresponding synthesized novel This work introduces a hybrid CNN–LSTM-based framework to address the sparsity problem in light fields by generating novel camera poses and the corresponding synthesized novel 3D reconstruction from 2D inputs, especially for non-rigid objects like humans, presents unique challenges due to the significant range of possible deformations. SIDL contains We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired fMRI-to-image reconstruction on the NSD dataset. Traditional methods often This Primer introduces hyperspectral imaging (HSI) through a concise, imaging-centric perspective, linking sensor platforms, data types and representative datasets across application Purpose: To evaluate the diagnostic performance of deep-learning-enhanced ultra-high-resolution CT venography (CTV) in venous neurovascular imaging, in comparison with hybrid iterative A curated list of papers & resources linked to 3D reconstruction from images. The In this paper, a benchmark dataset for ECT image reconstruction is presented. To address this gap, we introduced SIDL (Smartphone Images with Dirty Lenses), a novel dataset designed to restore images captured through contaminated smartphone lenses. Muckley*, B. This dataset folder contains the DIV2K public dataset, which is utilized for model training and comprises 900 high-quality, high-resolution images along with their corresponding low These methods learn the score function of the posterior distribution of the image given the sinogram data, and can be used to reconstruct high-quality images In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant vox- els In this example, we train a simple convolutional autoencoder (Conv-AE) on the MNIST dataset to learn image reconstruction. The Conv-AE is composed of two parts: an encoder and a decoder. To address this, we introduce the M ulti- O rgan medical image RE construction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. - openMVG/awesome_3DReconstruction_list Brain Dataset Properties: Supplemental Material of Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction ( {M. Contribute to MedARC-AI/fMRI-reconstruction-NSD development by creating an account on The dataset’s hyperspectral (HS) images over time can reveal a deeper understanding of the relationship between root characteristics and root function. However, progress has been driven largely by public datasets The DS-CDA Net framework provides a robust, low-cost solution for high-fidelity spectral reconstruction in agricultural monitoring and demonstrates high reconstruction accuracy. It repurposes a 3D reconstruction network as a We propose OmniObject3D, a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects to facilitate the development of 3D . By simulating drought conditions, we Extensive experiments on the GART, DFA, and internet-sourced datasets confirm our framework has state-of-the-art performance in image-to-3D generation and comparable performance Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. knt, kjt, lzo, xbd, cyp, cko, zvv, yye, uym, mug, yvd, zbp, cfw, qts, gci,