Issue title: DTU Quantitative results
 Issue body: Hi, I recently ran the training code in **Generic mode** and extract the mesh, then use the scripts
[https://github.com/jzhangbs/DTUeval-python](https://github.com/jzhangbs/DTUeval-python) to evaluate the **Chamfer Distance** for 15 test scenes according to the **Table 1** of your paper.
However I found the quantitative results I got are worse than those in **Table 1**, eg:

- scan24:   2.2(I got)  VS  1.68(Paper)
- scan37:  5.15(I got) VS  3.06(Paper)
- scan50:   3.2(I got)  VS  2.25(Paper) 

I use `--mode val` in command line to extract the mesh. In **conf** file I set the `test_ref_view = [23]`. In your paper I don't find the ref-view settings in experiment. So maybe you use other pairs.txt in Chamfer Distance test? Can you share some details in your experiment? 

Thanks a lot.

 Comments: 
 Comment 0: Hello. We use two pairs in each scene to do the evaluation.
Before calculating the chamfer distance for all the neural-based methods, we use the input image masks to clean the reconstruction results. This is because for all the neural-based methods, there will be many free surfaces in the background parts. Directly calculating the chamfer distance with ground truth point cloud and comparing it with the SOTA methods doesn't make much sense.

The evaluation input image pairs we used can be downloaded here: https://connecthkuhk-my.sharepoint.com/:u:/g/personal/xxlong_connect_hku_hk/EU22HEv48nRLnnnliRvJNA0BILozsMLbhsnMQh1WZLY5kg?e=Lh7kWM

I will upload the full evaluation code this week.
 Comment 1: > Hello. We use two pairs in each scene to do the evaluation. Before calculating the chamfer distance for all the neural-based methods, we use the input image masks to clean the reconstruction results. This is because for all the neural-based methods, there will be many free surfaces in the background parts. Directly calculating the chamfer distance with ground truth point cloud and comparing it with the SOTA methods doesn't make much sense.
> 
> The evaluation input image pairs we used can be downloaded here: https://connecthkuhk-my.sharepoint.com/:u:/g/personal/xxlong_connect_hku_hk/EU22HEv48nRLnnnliRvJNA0BILozsMLbhsnMQh1WZLY5kg?e=Lh7kWM
> 
> I will upload the full evaluation code this week.

Hi, @flamehaze1115 ,thanks for your reply.
- I have noticed the [testing dataset](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/xxlong_connect_hku_hk/EU22HEv48nRLnnnliRvJNA0BILozsMLbhsnMQh1WZLY5kg?e=Lh7kWM) has **set0** and **set1**. What's the difference between them? Maybe **set0** _corresp._ **lod0**, **set1** _corresp._ **lod1**? Or just select the best result in **set0 and set1**?
- the image mask files in [testing dataset](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/xxlong_connect_hku_hk/EU22HEv48nRLnnnliRvJNA0BILozsMLbhsnMQh1WZLY5kg?e=Lh7kWM) how to use?  shall we have other evaluation  scripts to utilize the image mask files？
 Comment 2: @flamehaze1115 

Sorry to bother you, but where is the full evaluation code?

Thanks a lot.
 Comment 3: @jerryxu9905 HI, guys, I meet the same problem. Did you solve it? 
The results of my run is bad, the details can be found in [this](https://github.com/xxlong0/SparseNeuS/issues/23#issuecomment-1264648535). 
Can you offer some suggestions?
{'source': 'GithubIssues', 'repo_name_with_owner': 'xxlong0/SparseNeuS', 'issue_id': 1361404733, 'repo_stars': 147.0}