How to calculate the value of metric PE without semantic infomantion?

[Hui-Yao]

Hello, Thanks for the impressive work of you and your team!

The output of your network are planes, lines and plane parameters, and there is no semantic infomation included in the output , so i`m very confuse about the computing method of metric PE, expecting for your reply.

Thanks again.

[CYang0515]

We use the detected planes(including plane semantic information, e.g. walls, floor,celling),lines and plane parameters to reconstruct the room layout as discribed in paper. The reconstruction code is here. Then, we use reconstruction results to generate the segmentation and the code is here.

[Hui-Yao]

#planes: a list [[x y x y score cls], ...] # cls is of [ceiling, floor, wall]

In your hypothesis, there is only one ceiling and one floor in the room model, but several walls exist, and the semantic information output by the plane detection branch can only distinguish three categories: ceiling, floor and wall. Therefore, for each wall, how do you determine the corresponding relationship between the predicted one and the GT one?
thanks for reply!

[CYang0515]

There are two ways to determine the corresponding relationship: 1. For each gt plane, match the Max-IOU prediction. The code is here 2. Use the Hungarian Alg. to match. The code is here

[Hui-Yao]

thanks a lot! At 2021-11-06 11:21:10, "CYang0515" ***@***.***> wrote: There are two ways to determine the corresponding relationship: 1. For each gt plane, match the Max-IOU prediction. The code is here 2. Use the Hungarian Alg. to match. The code is here — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub, or unsubscribe. Triage notifications on the go with GitHub Mobile for iOS or Android.