Comments (6)
Hi, thanks for your attention.
For the color prediction of the background, I think setting input_views_num to 4 will give better results. (The video in project page is rendered with input_views_num = 4)
https://github.com/zju3dv/ENeRF/blob/master/configs/enerf/enerf_outdoor/actor1_path.yaml#L5
For depth prediction for shadows, try setting background rendering mode to foreground images blending.
This produces reasonable depth prediction results. However, it will introduce some ghost rendering artifacts near the people.
src_inps=batch['src_inps']
https://github.com/zju3dv/ENeRF/blob/master/lib/networks/enerf/network_composite.py#L139
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Following the instructions, I also found the poor quality of result video of outdoor dataset, after setting input_views_num to 4. The PSNR after 50 epochs is about 27, which is far lower than zjumocap dataset. The modified parameters are as below:
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The ZJU-Mocap dataset got a high PSNR because it is a simple dataset.
ENeRF-Outdoor is more challenging, PSNR of 27 is not low for this dataset. If you produce similar rendering results to the project page, it should be a good indication that your usage are correct.
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Thank you for your explanation! However, my rendering result is far poorer than project page, the quality is basically the same as @ricshaw provided in this issue. I compared the properties of the saved videos, the project page video is 48MB and the video I saved from run.py is only 8MB. Does the video quality difference caused by the video saving process?
color.mp4
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Thanks. I don't think the saving process will affect the quality a lot. I think the quality of this rendering video is approaching the quality of the video in the project page. The main artifacts seem to come from the edges of the picture, which may because there are unseen regions in these views.
There are some differences between the released code and the code for generating rendered video on the project page. The released code uses the bbox generated by visual hull, while the bbox from the rough esimate of the 3D key points of the human body was used before. I will try to release the model and the corresponding bbox to help you fully reproduce the rendering video on the project page.
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Thank you so much for your help!
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Related Issues (20)
- custom outdoor dataset
- Issues on evaluation HOT 4
- Doubt with homo_warp
- How to make bounding box data? HOT 1
- Strange error while funetunning zjumocap HOT 1
- Super low GPU/CPU usage while training HOT 1
- Error in DTU Eval HOT 1
- Composition of the enerf-outdoor dataset HOT 1
- Run Enerf on a self-made zju-mocap HOT 4
- run gui_human.py on my own dataset. Problem with visualization
- About the camera? HOT 2
- Camera Color Calibration
- Properly formatted annots.npy file HOT 1
- 关于zjumocap_train.yaml文件下某些项的作用 HOT 1
- Question about config file HOT 3
- weird result after visualizing on the ENeRF-Outdoor dataset
- Problems with visualizing outdoor video HOT 1
- Two questions regarding the video rendering accuracy and the number of inference cameras for Enerf Outdoor. HOT 1
- Is camera radial distortion taken into account? HOT 1
- The Link of pretrained model from dtu_pretrain has expired HOT 1
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