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DeclutteringDefurnishDefurnishingMatterport 2024 Fall ReleaseMatterport Defurnish Tool

Matterport Digital Twin Example that Includes Defurnish Tool20317

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Matterport 2024 Fall Release | Matterport News

Video: Matterport 2024 Fall Release | Video courtesy of Matterport YouTube Channel | 1 October 2024

Matterport Digital Twin that includes Declutter Tool | In this Matterport digital twin, you can declutter (Hide Furniture / Show Furniture) using the Matterport Defurnish Tool in the bottom left corner. | Matterport Digital Twin courtesy of Matterport | 1 October 2024

Matterport Digital Twin Example that Includes Declutter Tool

Hi All,

In this Matterport digital twin (above), you can declutter (Hide Furniture / Show Furniture) using the Defurnish Tool in the bottom left corner.

Your thoughts?

Best,

Dan

P.S. WGAN Forum Related links:

WGAN Forum posts tagged: Matterport 2024 Fall Release
Matterport Fall Release: Defurnish a Home in 1-Click Tool, Merge Model Tool
Post 1 IP   flag post
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Nail Soup Media
Sarasota, Florida
GlennTremain private msg quote post Address this user
WOW!
Post 2 IP   flag post
MeshImages private msg quote post Address this user
The Declutter is really impressive!

I have just ordered my test version for one luxury apartment. We will see how good it processes...
Post 3 IP   flag post
malfie private msg quote post Address this user
They clearly state this works best on a house type of scan.
But if you want to see what happens when you do it on a small scan which I did on a fair last year, check this one out:

Post 4 IP   flag post
DigitalReplica3D private msg quote post Address this user
The decluttering tool is obviously in the beta stage. I demoed it with a couple of my MP spaces. Floors come out crooked and bowed. Wood flooring that goes west to east now became a patterned floor with angles. I had the front door and fire place mantel completely disappear. One of the closets became a hallway. A view looking out the slider window to the outside became an interior room. My test subjects admittedly had a lot of clutter so I can see issues trying to eliminate it and replace the background accurately.

I know we have been looking for a way to eliminate the camera caught in view of a mirror. Some good news is the declutter tool does just that. We just need to have the removed camera, not show up in the fully furnished views. I'll be reporting to MP the issues I'm seeing. Perhaps an option to pick and chose the wall or area that needs decluttered. Hope that in time, the tool will be up to speed to offer a client.



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For this to work as good as possible, Matterport has to develop this tool to correlate 3D data in the process of calculating and preserving the proper surfaces that are, and are not, walls and permanent structures. Clearly, at this point, the declutter tool is applying AI to each 360 pano independent of all the others, so it's processing out what it thinks is "furnishings" without checking other angles, with each of their point cloud 3D data sets, to see if its 2D guesses make sense. Here I'm referring to the 360 panos as "2D" because they provide no parallax information. Each pano is a view from only one point in space.

Here are 3 images taken from the example space. First the original furnished shot, then with red marks of errors, and finally the decluttered version. For example, note how the recessed face of the kitchen island (4 stools) is warped, with the far end missing altogether. 3D data from multiple other nodes would have clearly indicated what needs to be removed and what is permanent. In other views, the missing far side is still there. Also look at the jog in the wall at right side of the window wall. At the ceiling, the jog remains, but at the floor it's gone. Multiple alternate nodes COULD inform the declutter tool of the proper preservation of the wall jog, but this is ignored.

Other issues, the cooktop range has disappeared, one hanging sconce likewise, and even a window air conditioner on the building across the street is removed. The "guesses" of the AI are entertaining, I'll grant you that.

Potential is there, but correlating the point cloud data across all nodes of a space is essential.






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