VaishKS
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KITTI dataset horizon line extension proposed in the paper "Temporally Consistent Horizon Lines"
Source code of paper, Detecting Vanishing Points using Global Image Context in a Non-Manhattan World.
《IEEE TNNLS》RORNet: Partial-to-Partial Registration Network With Reliable Overlapping Representations
PlueckerNet: Learn to Register 3D Line Reconstructions
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
A personal list of papers and resources of image matching and pose estimation, including perspective images and panoramas.
Implementation of ViViT: A Video Vision Transformer
PyTorch Implementation of "Large-Scale Image Retrieval with Attentive Deep Local Features"
PyTorch implementation of a collections of scalable Video Transformer Benchmarks.
[SuperGlue: Learning Feature Matching with Graph Neural Networks] This repo includes PyTorch code for training the SuperGlue matching network on top of SIFT keypoints and descriptors.
Interview questions to ponder related to computer vision.
Code Release for LF-Net: Learning Local Features from Images
Official Pytorch implementation of GLAMpoints
This is an official implementation for "Video Swin Transformers".
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
official Pytorch implementation of ICCV 2021 paper FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting.
The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"
Release of the pretrained S3D Network in PyTorch (ECCV 2018)
Convolutional neural network model for video classification trained on the Kinetics dataset.
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO