Towards an efficient 3D model estimation methodology for aerial and ground images
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Updated
Oct 2, 2017
Towards an efficient 3D model estimation methodology for aerial and ground images
Project work from 3D Computer Vision course
The toolbox to run and evaluate reconstruction algorithms
Unstructured multi-view depth estimation using mask-based multiplane representation
[CVPR'20] Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement
Quick lookup for BlendedMVS scenes
Images dataset for 3D reconstruction
Code for "DeepVideoMVS: Multi-View Stereo on Video with Recurrent Spatio-Temporal Fusion" (CVPR 2021)
Joint Bilateral Upsampling with C++ and CUDA
Python scripts for converting SFM output into MVS input
A simple C++ algorithm for converting depth and normal maps to a point cloud
MVSNet: Depth Inference for Unstructured Multi-view Stereo using pytorch-lightning
A modern C++/CUDA implementation of state-of-the-art Multi-View Stereo (MVS) algorithms, open to community contributions.
Recreating a 3D scene using images taken from several point of views.
[CVPR 2022] Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation
Official code of IterMVS (CVPR 2022)
Camera calibration tool in Python + OpenCV
[ICCV 2021 Oral] NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereo
Official code of PatchmatchNet (CVPR 2021 Oral)
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