Response to reviewer comment (1/2)
We thank reviewer `sfxx` for increasing their initial scores and for taking the time to engage in the discussion period.
**We want to highlight that most of reviewer's additional concerns and questions were not raised in their initial review**. While we would have been happy to address them in our rebuttal, we do so in the following response:
> Novelty
We refer the reviewer to the list of contributions mentioned in our rebuttal. Although we do not consider the use of EDM alone to be one of our major contributions, we don't employ it as a simple *drop-in* replacement for DDPM and discuss the relevance of this design choice in the context of indoor scene synthesis in our main submission, L132-139.
> Experiments
We evaluate our method on the standard dataset for 3D indoor scene synthesis, **which is the one used in the most recent baselines** [1, 2], including the COFS [3] paper mentioned in the reviewer's comment.
**We also want to emphasize that we actually view the performance of our method** (demonstrated by our qualitative and quantitative evaluations), **and its ability to generalize to complex / unseen floor plans** (as shown in Figure 2 of our rebuttal `PDF`) **as additional strengths in the light of the limited number of training samples**.
Finally, **generative diffusion models** are known to **scale favorably well** to larger training datasets [4].
> Comparisons
As stated in our main submission L219, we chose to evaluate DeBaRA against established baselines from **different model *families*** (ATISS, i.e., autoregressive ; DiffuScene / LEGO-Net, i.e., denoising-based ; LayoutGPT, i.e., LLM-based).
We can read in the COFS paper: "*Our model thereby **extends the baseline ATISS** with new functionality while **retaining all its existing properties and performance***", which can be verified in their experimental evaluations. On the other hand, **our method largely outperforms ATISS**, as quantitatively verified in **Table 1** and **Table 2** of our submission, and observed in **Figure 3**.
The GRAINS method **only supports rooms having four walls** in its predicted layouts, while an important feature of our approach is that it takes into consideration complex (i.e., non square) input floor plans. As a result, it is not applicable to a significant number of scenes from our test set. In contrast to DeBaRA, SceneHGN proposes to generate scenes at **the object part-level**, which requires a custom dataset. However, we are willing to evaluate the 3D layout generation capabilities of the method in the revision of our manuscript.
> Evaluation Metrics
Unlike what is stated in the reviewer's comment, KL Divergence is not measured in our study as object's semantic categories are not part of DeBaRA's prediction space. For the same reason, measuring object concurrences may not be a relevant addition to our paper.
Also note that **FID** and **KID** are known to evaluate **both** the **diversity** and the **fidelity** of the generated content. Non-diverse generation results wouldn't comprehensively capture the distribution of real scenes, which would directly penalize the FID and KID scores. Additionally, a better **SCA** score reflects more plausible layouts, as they are *harder* to distinguish from *real* ones.
The superiority of denoising-based approaches in capturing symmetry / alignment patterns compared to e.g., autoregressive methods in the context of scene synthesis has been extensively studied by previous work [1, 2].
We also include in our submission's appendix additional indicators measuring the **validity** of generated layouts w.r.t. the provided floor plan (Table 5).
> Rendering Quality
Our scene renderings, with objects colored according to their semantic categories, have been included to help readers appreciate the quality, diversity and validity of generated layouts, while easily **distinguishing** different objects. We do not claim our renderings to be at the level of those produced by an artist. We used the rendering pipeline provided by the official implementation of DiffuScene (which builds upon the one of ATISS). Our rendering quality is therefore on par with the one of this recently published work. However, also note that our method could be employed along more advanced rendering engines.
We will follow your suggestion and include additional qualitative results featuring **textured objects and floors**, which can be obtained using our current rendering pipeline.
> It would be insightful to investigate whether this diffusion-based method can produce more diverse scenes
Please see our previous answer regarding FID / KID.
> How many generated/GT scenes do you use for evaluation?
As stated in our main submission, evaluation metrics are computed across each **test** subset (L244), following the splits described L225. For each test subset, we generate the same number of scenes as the number of *real* ones.