shivamduggal4 / tars3d Goto Github PK

Hi, thank you for your excellent and interesting work, but I can't find the code that implements precision, recall and F-score.
So, can you tell me where the implementation code is?
thanks.

Hi Shivam:
I have some questions to test on the new image. If I need to recreate a network image of a new chair, do I need other inputs besides that image?
In addition, I also saw a problem with camera perspective from another issue. For a new image, do we need a camera parameter as an input in order to convert 3 D coordinates? If so, is it possible to use another network to predict this part of the camera parameters (I think it is possible to fix the world coordinates and camera parameters, just predict the rotation matrix and the offset matrix)?
Or did you write this part of the code to predict new network images?
Many thanks in advance.

Best,
Yang

Good morning,
Congrats on the very interesting paper! And very glad to read the nice code after seeing your CVPR poster.
I have one dataset composed of 3D meshes and their render. And would like to train and evaluate your method for comparison with our work.
If I understood well I could copy one of the scripts folder and adapt it ?
But should I rewrite all the dataloader classes and script or is there a fastest and simple way to do it.
The dataset is quite simple : mesh + renders + pose and camera parameters.

Thank you in advance :)
Justin

Hi Shivam,

I have ran the evaluation for the airplane and car categories of ShapeNet. The reconstructed meshes are quite impressive. I have two questions regarding the ShapeNet evaluation.

(1) For my evaluation on ShapeNet, the loss of car is 1.837e-02, the chamfer distance of car is 0.0124|0.0240. The loss of airplane is 2.289e-02, the chamfer distance of car is 0.0182|0.0160. The chamfer distances of my evaluation are smaller than that reported in the paper. For example, the chamfer distance of airplane is 0.194|0.152 in the paper. Could you please let me know what might cause the differences?

(2) I have to change the name of the ground truth point cloud from "pointcloud3.npz" to "pointcloud.npz" in this file for ShapeNet, since the name of the ShapeNet pointcloud is "pointcloud.npz" instead of ""pointcloud3.npz". Is this fine?

Thank you very much!

Hi @ShivamDuggal4

Thanks for your existing work. I just tried to play your model but didn't find out how to get the correct cd and f-score. (e.g. the distance or squared distance, the distance times 10 or not, point number).

Here is my implementation:

d1, d2, _, _= distChamfer(gt_pc, pred_pc)
d1 = d1.sqrt()
d2 = d2.sqrt()
precision = (d1<0.1).sum()/d1.shape[0]
recall = (d2<0.1).sum()/d2.shape[0]

Could you please check this or release your test script?

Thanks a lot!

Thanks for your great job. Could tars3d train on other dataset? like dog something?