Image Reconstruction Dataset. - sicxu/Deep3DFaceRecon_pytorch This dataset contains CoarseDat

- sicxu/Deep3DFaceRecon_pytorch This dataset contains CoarseData (if you are looking for the expression model, find it here) and FineData augmented from 3131 images of 300-W with the method described in the paper CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images. 2017-07-19: Initial release of Apr 1, 2025 · Trackerless 3D Freehand Ultrasound Reconstruction (TUS-REC2025) Challenge Reconstructing 2D Ultrasound (US) images into a 3D volume enables 3D representations of anatomy to be generated which are beneficial to a wide range of downstream tasks such as quantitative biometric measurement, multimodal registration, and 3D visualisation. 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 and exploiting their correlated patterns. Dec 18, 2025 · Photon-counting CT has gained significant attention in recent years; however, publicly available datasets for spectral reconstruction and deep learning training remain limited. Jan 29, 2020 · A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented. Reconstructing natural images from the CNN features decoded from the brain with deep generator network (DGN); reproducing results in Figure 2. . we generate a large-scale raw-clean pairwise depth image dataset that can be used for supervised learning of depth image enhancement, by applying the state-of-the-art dense 3D surface reconstruction on RGB-D streams. Images dataset for 3D reconstruction. 3131383). We provide seven scripts that reproduce main figures in the original paper. However, for image reconstruction, the dataset should contain an input image and a target image, which are simply the same. In particular, it contains a large-scale simulated training dataset composed of 31000 images for the The downloaded MNIST dataset format is for classification, which means each sample contains an image and a label (the digit drawn in the image). It contains multiple datasets used for training and testing, as well as the trained models and results (predictions and metrics). 1109/TUFFC. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. We provide seven scripts that reproduce main figures in the original paper. The method is then evaluated using certain 3D reconstruction datasets. - Yashas120/Multiview-3D-Reconstruction Lastly, Stoffregen, Scheerlinck et al. May 22, 2024 · fMRI-to-image reconstruction on the NSD dataset. Reconstruction was also used to identify the presented image among millions of candidates. [7] recently highlighted that, when train-ing with ground truth, the statistics of the training dataset play a major role in the reconstruction quality. A full processing stream for MR imaging data that involves skull-stripping, bias field correction, registration, and anatomical segmentation as well as cortical surface reconstruction, registration, and parcellation. News More details are available in the changelog. Contribute to alicevision/dataset_monstree development by creating an account on GitHub. Sep 4, 2023 · We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10. Using traditional image processing techniques to construct 3D point cloud of objects. 2019-06-16: Added the SLAM Benchmark. Apr 23, 2024 · With the emerge of deep learning (DL)-based medical image processing methods, some generative adversarial network (GAN) methods have been established related with CT reconstruction from only few With exten-sive quantitative and qualitative experiments on diverse im-age datasets, we demonstrate that the proposed method per-forms favorably against state-of-the-art single-image HDR reconstruction algorithms. Dissipation of heat from the mantle is the original source of the energy required to drive This repository contains the data related to the paper “CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging” (10. The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance. 2017-10-04: Extension with more data. They showed that a slight change in the training statistics of E2VID leads to significant improvements across multiple datasets. Jul 19, 2017 · ETH3D Benchmarks SLAM benchmark Stereo benchmark Open Source Code See the ETH3D project on GitHub. Each red dot is a measuring point and vectors show direction and magnitude of motion. 2021. 2018-02-05: Open source release of the dataset pipeline. Incremental Structure from Motion (SfM) is used, a popular SfM algorithm for 3D reconstruction for reconstruction. Tectonic plates are able to move because of the relative density of oceanic lithosphere and the relative weakness of the asthenosphere. Oct 30, 2025 · To address this, we introduce the Multi-Organ medical image REconstruction (MORE) dataset, comprising CT scans across 9 diverse anatomies with 15 lesion types. A PyTorch implementation. Dec 18, 2025 · To address this gap, we present a cone-beam photon-counting CT (PCCT) dataset acquired using a custom-built micro-PCCT system and 15 walnut samples. A learning-based TSDF fusion Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set (CVPRW 2019). Contribute to MedARC-AI/fMRI-reconstruction-NSD development by creating an account on GitHub. Hence, using the code below, we define a new dataset that wraps an MNIST dataset and provides an image as an input and sets 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 random images. 2018-04-16: Added pre-rendered depth maps for training datasets for convenience. Plate motion based on Global Positioning System (GPS) satellite data from NASA JPL. We perform thorough evaluation of the proposed dataset, which enables significant qualitative and quantitative improvements of the state-of-the-art HDR image reconstruction methods. We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video.

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