This tutorial describes the image matching parameters Apply masks to Key/Tie points and describes the cases, where Tie point masking may be reasonable.

  • If Apply masks to Key points selected - masked areas are excluded from feature detection procedure independently for each photo. 

  • If Apply masks to Tie points selected - certain tie points are excluded from the alignment procedure. Effectively this implies that if some area is masked at least on a single photo, relevant key points on the rest of the photos picturing the same area will be also ignored during the alignment procedure (a tie point is a set of key points that have been matched as projections of the same 3D point on different images). This can be useful to suppress background in turntable-like shooting scenarios with few or even with a single mask. Two examples of difficult fo alignment turntable-like datasets will be discussed below. 

Project: Oenochoe on the bedsheet

80 photos: 

This dataset doesn't contain the photo of the background without the object, because the photos were taken from different positions and with different view angles, and as a result, the photos observe different parts of the background.

Except for the static background this dataset has another suboptimality - lighting conditions could have been better. In an ideal case, you need uniform diffuse light.

  • Apply masks to None 

If you just run Workflow > Align Photos..., you will see cameras aligned relative to the white background bedsheet: 

To avoid this, you need to mask the background surface. It is enough to mask the background with Intelligent Scissors on only two photos - a photo with the bedsheet's upper part and the photo with the bedsheet's bottom part. 

a single mask is not enough, because there is no single camera observing full bedsheet at once:

  • Apply masks to Tie points 

With just two masks and parameter Apply masks to Tie points all 80 photos were successfully aligned: