You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Sep 2, 2024. It is now read-only.
Can anyone shed some light on why certain techniques (like those inspired by MVSNet) appear so abundantly on the leaderboard, but others, like MSI (multi-sphere images) or NeRF (neural radiance fields) don't?
I think that the depth maps generated by for example instant-ngp or MipNerf are of high quality and could result in a top position on the leaderboard. (I assume Colmap is used for the SfM). So, the leaderboard feels a bit incomplete.
Has anyone tried to apply NeRF on this dataset and compared it to the methods on the leaderboard?
I appreciate any insight on this.
The text was updated successfully, but these errors were encountered:
Sign up for freeto subscribe to this conversation on GitHub.
Already have an account?
Sign in.
Can anyone shed some light on why certain techniques (like those inspired by MVSNet) appear so abundantly on the leaderboard, but others, like MSI (multi-sphere images) or NeRF (neural radiance fields) don't?
I think that the depth maps generated by for example instant-ngp or MipNerf are of high quality and could result in a top position on the leaderboard. (I assume Colmap is used for the SfM). So, the leaderboard feels a bit incomplete.
Has anyone tried to apply NeRF on this dataset and compared it to the methods on the leaderboard?
I appreciate any insight on this.
The text was updated successfully, but these errors were encountered: