A curated list of tutorials, papers, software related to multi-view stereo.

A Python package housing a collection of deep-learning multi-modal data fusion method pipelines! From data loading, to training, to evaluation - fusilli's got you covered 🌸

The official implementation of the paper, "SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning"

This repo includes the source code of the fully convolutional depth denoising model presented in https://arxiv.org/pdf/1909.01193.pdf (ICCV19)

Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease (AMIA 2018)

a Pytorch library for multi-view 3D understanding and generation

[NeurIPS 2023 Spotlight] Code for "Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion"

[CVPR'25] Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

Official code for "SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation"

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

[MICCAI2021] This is an official PyTorch implementation for "Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation"

Experimental repo for modelling Articulated Object Neural Radiance Field

[TKDE] Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

Official implementation of our MICCAI 2022 paper "mulEEG: A Multi-View Representation Learning on EEG Signals"

[ICLR 2023] Official code for the paper "Identifiability Results for Multimodal Contrastive Learning"

Awesome Semi-supervised Multi-view Classification is a collection of SOTA, novel semi-supervised multi-view classification methods (papers, codes).

Deep Learning Approach for UWB Applications (IEEE Journal of Biomedical and Health Informatics)

Adaptive Confidence Multi-View Hashing

MultiGraphGAN for predicting multiple target graphs from a source graph using geometric deep learning.

Code for "Long Horizon Forecasting With Temporal Point Processes", WSDM 2021