Dear Reviewer m8Xp,
Thank you for your follow-up questions, allowing us to clarify further.
### **Comparison with RawNeRF in the Training Views:**
**Why RawNeRF Results Are Not in Fig. R1**
- The extensive training time required for RawNeRF (**approximately a week per scene**) limited our ability to complete all desired scenes within the rebuttal period. We used all three A100s to train the testing scenes in RawNeRF dataset and report the results in Table R2. As a result, Fig. R1 could not include additional RawNeRF comparisons for those specific viewpoints.
- The notable color bias observed between RawNeRF visualizations and ours in Fig. R1 might be attributed to differences in white balance settings. In RawNeRF's test sets, the output colors are finely adjusted using reference images. However, we can not adjust the color in Fig. R1 in such a way since it is not feasible for training views due to the absence of comparable reference data.
**Concerning Full-view Results Comparison:**
- Our study primarily focuses on **few-shot scenarios**, yet we still provide full-view results for completeness. Our RawNeRF is trained using exactly the **official code**. The **visualization** of our result **is similar** with that in the RawNeRF paper (the third row in the RawNeRF Fig. A1), e.g., the appearance of the artifacts. Our implementation achieves **almost the same** results in the rendered RGB space as follows. Therefore, we believe the correctness of our implementation given the above consideration.
||PSNR|SSIM|LPIPS|
|---|---|---|---|
|**RawNeRF in Paper (Table 1)**|23.53|0.536|0.501|
|**Ours (Table R2 in Rebuttal)**|23.53|0.538|0.500|
- As depicted in Fig. R2, our results achieve **comparable reconstruction quality to RawNeRF**. However, a significant enhancement in rendering speed from **0.1 FPS to 134 FPS** underscores the practical improvements our method offers. Moreover, for consistent assessment across all methods, the same color correction applied in RawNeRF is used for quantitative and qualitative comparisons in our main paper.
### **Trade-off Between Preserving Sharp Details and Reducing Artifacts, Enhancing Rendering Speed, and Boosting PSNR**
We want to clarify that our method shows significant improvements, particularly in scenarios with limited training views, as you noted. We have effectively reduced artifacts in such cases, as seen in the first row of Fig. R1. However, in full-view settings, some blurring can occur, as mentioned in our manuscript's limitations. This is a **trade-off between preserving sharp details and reducing artifacts, boosting rendering speed, and improving PSNR**. For instance, in the second row of Fig. R1, the rendering speed of HDR Scaffold-GS versus our method is **52 FPS** versus **96 FPS**, respectively. In the testing views, we increase the PSNR of HDR Scaffold-GS from **58.08dB** to **59.49dB**.
### **ELD/PMN Domain Gap**
As mentioned in our rebuttal, the domain gap is not only due to bit depth differences but also other sensor characteristics, such as the color response curve. This **color bias is also noted in the RawNeRF paper**:
> "Since each method was trained on raw data from a different source, they impart different color tints to the output ... we calculate a per-color-channel affine transform that best matches each method’s raw output to the ground truth raw image."
In training scenes, we do not have the ground truth to perform a per-color-channel affine transform, which may result in the color bias observed in our response figures.
### **The Analysis**
The reason to introduce the denoising prior when \(N\) is small is to reduce the high-frequency artifacts, e.g., needle-like artifacts in HDR-Scaffold. As shown in the figures, introducing a denoiser effectively reduces these artifacts. The color bias is mainly due to the DC component, i.e., the bias you mentioned.
For the reason of using a self-supervised manner, the rationale is as follows:
- **Self-supervised denoising is a meaningful topic** that many researchers are working on. It has unique advantages in cases where the clean image domain is unavailable, e.g., 4D Gaussian modeling of dynamic scenes in the dark.
- While the noise model in raw space is relatively clear, only having a noise prior cannot obtain the clean image/scene. Similar to the deep image prior (DIP) \[a\], **we are the first to find that 3DGS also has a similar 3D scene prior** as shown in our analysis.
- While using 3DGS alone can somehow denoise the scene by controlling the number of iterations as in Fig. 3, it has the risk of overfitting to the noise. We are also the first to **combine the noise prior and 3DGS prior in a self-supervised manner**. In such a way, we can avoid overfitting to the noise and achieve better results, e.g., superior PSNR and FPS.
We hope these address your concerns. Please feel free to reach out with further questions.
\[a\] Deep image prior, CVPR 2018