Dev-MerB

Dev-MerB's Stars

• graphdeco-inria/gaussian-splatting

  Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"

  Language:Python14.6k1189881.9k

• HumanAIGC/AnimateAnyone

  Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation

  14.5k67392973

• zju3dv/NeuralRecon

  Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

  Language:Python2.1k50133301

• wasserth/TotalSegmentator

  Tool for robust segmentation of >100 important anatomical structures in CT and MR images

  Language:Python1.5k27345253

• sxyu/pixel-nerf

  PixelNeRF Official Repository

  Language:Python1.4k2375198

• Awesome-Image-Registration-Organization/awesome-image-registration

  image registration related books, papers, videos, and toolboxes

  1.3k334167

• yangjiheng/nerf_and_beyond_docs

  1k62038

• CERN/TIGRE

  TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

  Language:MATLAB57644404189

• caiyuanhao1998/SAX-NeRF

  "Structure-Aware Sparse-View X-ray 3D Reconstruction" (CVPR 2024)

  Language:Python457123119

• EricLee0224/awesome-nerf-editing

  🧙🏻‍♂️A list of papers curated for you to dive into the Awesome Radiance Field-based 3D Editing.

  42821013

• AlgoHunt/VQRF

  Official implementation of our CVPR 2023 paper "Compressing Volumetric Radiance Fields to 1 MB"

  Language:Python317111019

• MrGiovanni/SuPreM

  [ICLR 2024 Oral] Supervised Pre-Trained 3D Models for Medical Image Analysis (9,262 CT volumes + 25 annotated classes)

  Language:Python2423148

• JunMa11/AbdomenCT-1K

  The official repository of "AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?"

  Language:Python227112835

• MrGiovanni/AbdomenAtlas

  [NeurIPS 2023] AbdomenAtlas 1.0 (5,195 CT volumes + 9 annotated classes)

  Language:Python2147816

• hugoycj/torch-bakedsdf

  An unofficial pytorch implementation of BakedSDF

  Language:Python1928138

• xmindflow/Awesome-Implicit-Neural-Representations-in-Medical-imaging

  [ICCV 2023] A curated list of resources on implicit neural representations in Medical Imaging

  1698217

• fabio86d/HipHop_2D3Dregistration

  2D/3D registration between CT/MRI or STL models and X-ray images (November 2018)

  Language:C++1506836

• eigenvivek/DiffDRR

  Auto-differentiable digitally reconstructed radiographs in PyTorch

  Language:Python148618720

• kylekma/X2CT

  Language:Python14673049

• Ruyi-Zha/naf_cbct

  "Neural Attenuation Fields for Sparse-View CBCT Reconstruction" (MICCAI 2022 Oral)

  Language:Python11922421

• eigenvivek/DiffPose

  [CVPR 2024] Intraoperative 2D/3D registration via differentiable X-ray rendering

  Language:Python11854214

• liyues/NeRP

  Implicit Neural Representation Learning With Prior Embedding for Sparsely Sampled Image Reconstruction

  Language:Python881712

• NagabhushanSN95/ViP-NeRF

  Official code release accompanying the paper - "ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields"

  Language:Python682107

• christianpayer/MedicalDataAugmentationTool-HeatmapRegression

  Language:Python581924

• xmed-lab/DIF-Net

  MICCAI 2023: Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

  Language:Python581811

• xmindflow/INCODE

  [WACV 2024] INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings

  Language:Jupyter Notebook36326

• AnderBiguri/PhDThesis

  A.Biguri PhD thesis: Iterative Reconstruction and Motion compensation in Computed Tomography on GPUs

  Language:TeX31405

• wuminye/ImplicitGS

  Implicit Gaussian Splatting Code

  Language:Python24331

• MIAGroupUT/IMPLICIT-CELL-SURFACES

  Code for MedIA 2024 paper "Generative modeling of living cells with SO(3)-equivariant implicit neural representations" and MICCAI 2022 paper "Implicit Neural Representations for Generative Modeling of Living Cell Shapes"

  Language:Python10100

• skaraoglu/Photometric-Stereo

  In this paper, we implement a standard photometric stereo algorithm. With the albedo being unknown and not constant for the images, this program takes multiple images and light source direction information as an input and produce the albedo map, surface normals map, heights map and 3d plot of the surface for provided input. All the images used in this project are provided in the homework assignment and stored in "src" folder. Several output plots shown in the work are stored in the "out" folder.

  Language:Jupyter Notebook2011