Good Things Come to Those Who Wait: a more detailed look into merging 3-D models

The title of this blog sums up this past week pretty well. I work for OCPS as a middle school teacher and only have time at night or on the weekends to work on this project and no longer have the luxury to work whole days on classes as I have done in the past. This is prevalent because the merging processes took around 30-45 minutes each time I ran the process. I created many models this way before I realized an error in what I was doing. I could not separate the points based off of confidence as I learned last week because I rendered all the models with he advanced setting “calculate point confidence” turned off by default. Having this enabled is necessary to making the model clean as I will demonstrate in this weeks blog. This caused me to rerun these merging processes again, however enabling the “calculate point confidence,” caused processing time to increase even more from 30-45 minutes to between an hour to an hour and 15 minutes. Once more, if I realize there is an area of the model that is shaky and could use more photos then this waiting process has to start over again. This is why I describe this week as a lot of waiting as a single mistake can cost me a whole evening of just waiting.

                After hours of researching on the internet why I was having such difficulty merging models I discovered that Metashape has an automatic alignment method without the need of finding key points of similarity around the model. It took some time to get the hang of it but this made the process much easier by having the program itself detect the similarities.

                Once the models were merged I realized I forgot to cut away the table portions where the artifact was sitting before I merged the models but I was still able to cut it away after I merged them and it was not too much a problem considering the alternative was another hour of waiting time. I was now able to see the model in a heat map style of view that is organizing all of the points based of off how confident the program is on their placement. Red and orange dots are all those points that the program is unsure of and are therefore clouding the model giving it a dirty or fuzzy appearance. All those models that shift from green to blue are high confidence points where the program is confident in its placement. I was able to filter out the view to separate high and low confidence points and once there I could delete all low confidence points leaving behind a clean model.

This is the confidence map. The key in the lower left helps understand that the colder the color the higher the confidence value is.

The red and orange points are filtered out from the high confidence points and then deleted. These points are what clouds the model and gives it a fuzzy appearance due to their inaccurate location.

Only the high confidence points left behind

The dense point cloud after the confidence cleaning process. Notice how this is cleaner than any other dense point cloud I showcased thus far.

                From here I realized I would need to go back and take even more pictures on the bottom side of the model due to the lip of the conch being undefined and curved as opposed to a strong harsh edge. This artifact was included in the collection to be one of the more difficult model to create and so far it has and will continue to cost me more processing time as I try to get the inner fold of the shell to render but now with this streamlined process of cleaning the model I’m confident that this once will be completed as well.

The above photo shows the end product that is fully textured. This is the cleanest model I have made yet and looks perfect.

This is the problem mentioned in the above paragraph. The inner lip of the conch did not render any points due to a shortage of light and photos.