Pinned Repositories
3d-psrnet
Repository for 3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction [ECCVW 2018]
3D_modelling
Project for 3D reconstruction of object from point cloud and images
A23D
AI-links
cityjson
A JSON-based implementation of CityGML, easy-to-use and compact
CRF_denoising
Build a CRF model using Chainer for binary image denoising.
deep-learning-specialization-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
dtm_lab1
Interpolations
GeomAwareLoss
Code and Data used for conducting Research on my Masters thesis : Indoor 3D Reconstruction from a Single Image
indoor_positioning
This code was a part of synthesis project on using point clouds for indoor localisation focusing on ceilings
cgarg-tud's Repositories
cgarg-tud/GeomAwareLoss
Code and Data used for conducting Research on my Masters thesis : Indoor 3D Reconstruction from a Single Image
cgarg-tud/3D_modelling
Project for 3D reconstruction of object from point cloud and images
cgarg-tud/3d-psrnet
Repository for 3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction [ECCVW 2018]
cgarg-tud/A23D
cgarg-tud/AI-links
cgarg-tud/cityjson
A JSON-based implementation of CityGML, easy-to-use and compact
cgarg-tud/CRF_denoising
Build a CRF model using Chainer for binary image denoising.
cgarg-tud/deep-learning-specialization-coursera
Deep Learning Specialization by Andrew Ng on Coursera.
cgarg-tud/dtm_lab1
Interpolations
cgarg-tud/indoor_positioning
This code was a part of synthesis project on using point clouds for indoor localisation focusing on ceilings
cgarg-tud/kaolin
A PyTorch Library for Accelerating 3D Deep Learning Research
cgarg-tud/PlanarReconstruction
[CVPR'19] Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
cgarg-tud/ragstack_testing
cgarg-tud/ScanNet
cgarg-tud/Semantic-Mono-Depth
Geometry meets semantics for semi-supervised monocular depth estimation - ACCV 2018
cgarg-tud/superpixels-revisited
Library containing 7 state-of-the-art superpixel algorithms with a total of 9 implementations used for evaluation purposes in [1] utilizing an extended version of the Berkeley Segmentation Benchmark.
cgarg-tud/t81_558_deep_learning
Washington University (in St. Louis) Course T81-558: Applications of Deep Neural Networks