The accuracy of predicted sdf function

[1999kevin]

Hi, thank you for your wonderful job. I have tested the performance on your models and get some trouble about the accuracy.
Here is my test process:
I have a mesh model: a sphere with center at (0,5,0,5,0,5) and with radius 0.5. So this mesh surface contains the points such as [0.5, 1, 0.5], [1, 0.5, 0.5], [0.5,0.5, 1]
I use your code to train a model with following commands: python --net OctreeSDF --num_lods 5 --dataset_path my.obj --epoch 250 --exp-name test

And then I use the model to get some sdf predicted value around the points , [0.5, 1, 0.5], [1, 0.5, 0.5], [0.5,0.5, 1].
I make up three points sets: [0.5, i, 0.5], [i, 0.5, 0.5], [0.5,0.5, i] for i in numpy.linspace(0.997, 1.003, 1001)
I test the accuracy of the model with these three points sets and get some results:

I find the the sdf values change their sign at [0.5, 0.99895, 0.5], [0.999027, 0.5, 0.5], [0.5,0.5, 0.999027], which are about 0.001 from the ground truth. And at points 0.5], [1, 0.5, 0.5], [0.5,0.5, 1], the predicted sdf values are about 0.002, which is also too big compared with the 1e-6 training L2 loss.

Could you please give some explaination about this phenomenon? And is there any suggestion to improve the accuracy of the predicted sdf value?

I'm looking forward to your reply. It is quite important for me.