The rendering results look good but the mean PSNR, which is computed by evaluation/eval.py, is different with the result report in the paper. Specially, I evalled the result of kitty and used same lighting as training. The mean PSNR is about 22 while the PSNR in the paper is 36.45.
And here is the reason:
1) eval.py computes PSNR of images in linear space instead of sRGB space, i.e, dose not apply "I_out = I_in ^ (1/2.2)" described in Tab.3. Although Line 204 runs tonemap_img, the gamma is 1.0.
2) calculate_psnr in eval.py Line 396 only calculates MSE on masked area instead of the whole picture. mse = np.mean((img1 - img2)**2) * (img2.shape[0] * img2.shape[1]) / mask.sum() at Line 400 should be modified as mse = np.mean((img1 - img2)**2)
The rendering results look good but the mean PSNR, which is computed by evaluation/eval.py, is different with the result report in the paper. Specially, I evalled the result of kitty and used same lighting as training. The mean PSNR is about 22 while the PSNR in the paper is 36.45.
And here is the reason: 1) eval.py computes PSNR of images in linear space instead of sRGB space, i.e, dose not apply "I_out = I_in ^ (1/2.2)" described in Tab.3. Although Line 204 runs
tonemap_img, the gamma is 1.0. 2)calculate_psnrin eval.py Line 396 only calculates MSE on masked area instead of the whole picture.mse = np.mean((img1 - img2)**2) * (img2.shape[0] * img2.shape[1]) / mask.sum()at Line 400 should be modified asmse = np.mean((img1 - img2)**2)