Multi-View Stereo (MVS) algorithms aim at computing a dense 3D point cloud of the scene from a set of calibrated images, with given intrinsic parameters and known poses. While classical geometric methods are still the best performing approaches in this field (e.g. on the high-resolution ETH benchmark), an increasing number of deep neural networks started to appear in recent years, with incredible results. However, most of these works rely on ground truth depth for training, which is both difficult to obtain and not scalable to real-world situations. An emerging trend in learning-based MVS research is to train deep networks without…


[Disclaimer: 3D Reconstruction News is a series of blog posts in which I review the best contributions, at a given conference, to fields like depth estimation, MVS, SfM, VO, VSLAM and other cool acronyms related to large-scale outdoor 3D reconstruction from images]

Here we are with another main conference of the year, namely AAAI 2021 (you can find my previous post on WACV 2021 here). What’s new in the 3D reconstruction field? Here are my usual three takeaways:

  • Unsupervised multi-view stereo (MVS) is finally becoming something relevant.
  • Stereo matching networks are very stupid and not robust to adversarial attacks.
  • Monocular…

[Disclaimer: 3D Reconstruction News is a series of blog posts in which I review the best contributions, at a given conference, to fields like depth estimation, MVS, SfM, VO, VSLAM and other cool acronyms related to large-scale outdoor 3D reconstruction from images]

So, this year’s first computer vision conference (WACV 2021) is over. Did we get better at figuring out the 3D structure of the world from images alone? Kind of. Here are my three takeaways:

  • NASA uses 3D reconstruction to find safe landing sites on Mars.
  • Attention mechanisms meet multi-view stereo (again).
  • Monocular depth can be improved either with…

Marco Orsingher

PhD student trying to reconstruct the world in 3D

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