Thanks for your feedback.
Dear Reviewer 5i8r,
Thank you for your feedback and the opportunity to address your concerns. We fully acknowledge the significant contributions of previous PBR methods [7, 16, 17, 45] targeting inverse rendering, and we appreciate your references to recent methods combining ray tracing with 3DGS.
Our approach, however, addresses **the core balance issue between rendering and geometry quality** through a simple yet effective design, without complex regularizers, assumptions, or ray tracing, as detailed in our paper (L32-36, L52-53, Figure 1) and response to Q5. This focus differentiates our approach from inverse rendering techniques that often require additional regularizers or assumptions. In the Experiment section of our paper, we directly compared our method with a prior inverse rendering method [7] and showed our superior rendering quality and normal accuracy. We believe our method establishes a new baseline that could benefit the community. We respectfully emphasize that this explanation is crucial to understanding the motivation and advantages of our method.
Regarding the “environment map representation” and “split sum” [A], both of which assume the existence of a global environment map, we would like to clarify that our method does not use this representation. Instead, as comprehensively outlined in our main paper (L165-174, Figure 4, experiments) and our response to Q1, this assumption has limited applicability in real-world scenarios. To clarify further, Figure 4 clearly illustrates the disadvantage of relying on global environment maps, where [7] fails to capture specular effects but our method succeeds. Moreover, in the “Dr. Johnson” scene in “Deep Blending”, which involves multiple rooms without a global environment map, the previous method [7] failed, as shown in Table 1 of our paper.
We appreciate the suggestion that our work might be “better suited for graphics venues.” However, given the success of related work in the field of neural rendering and geometry modeling, such as IDR [B], Neural-PIL [C], NeuS [D], VolSDF [E], SAMURAI [F] and NDRMC [G] at NeurIPS, we believe our contributions align well with the conference’s scope, particularly in “Machine Vision.”
Thank you again for your thorough review.
Best regards,
Authors of Submission 8220
[A] Brian Karis. Real shading in Unreal Engine 4. SIGGRAPH 2013 Course: Physically Based Shading in Theory and Practice, 2013.
[B] Yariv, Lior, et al. "Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance." NeurIPS (2020).
[C] Boss, Mark, et al. "Neural-pil: Neural pre-integrated lighting for reflectance decomposition." NeurIPS (2021).
[D] Wang, Peng, et al. "Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction." NeurIPS (2021).
[E] Yariv, Lior, et al. "Volume rendering of neural implicit surfaces." NeurIPS (2021).
[F] Boss, Mark, et al. "Samurai: Shape and material from unconstrained real-world arbitrary image collections." NeurIPS (2022).
[G] Hasselgren, Jon, et al. "Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising." NeurIPS (2022).