Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning

Thank you sincerely for your valuable time and insightful comments.

W1: Human preference evaluation

Thank you for the suggestion. In response, we conduct a user study with 25 compensated volunteers, all with relevant backgrounds in 3D vision or computer science. Participants are asked to rate anonymized, randomly ordered layouts generated by different methods across the following three criteria:

Semantic Compliance: How well the layout matches the input text description.

Layout Rationality: Whether the spatial arrangement of objects is reasonable, functional, and typical of real-world scenes.

Physical Plausibility: Whether the layout is physically sound—for example, no floating or interpenetrating objects, and appropriate support relationships.

Our method consistently received the highest average scores across all categories, showing strong agreement with human preferences. This study complements the VLM-based evaluation and confirms that our method produces layouts that are not only quantitatively strong but also preferred by human evaluators.

We also include comparison with ATISS, DiffuScene, and Infinigen:

While baselines perform strongly overall, our method matches or surpasses their performance on certain scene categories, and additionally supports open-vocabulary generation.

W2: Rule-based baseline comparison (e.g., Infinigen-Indoors)

We include Infinigen-Indoors, ATISS, and DiffuScene as new baselines and compared its performance on bedrooms and living rooms with our method:

We also evaluate Infinigen in the user study. While it performs well, our method achieves similar or better results on specific scene types and supports open-vocabulary generation, which rule-based systems cannot easily accommodate.

Q1: Room shape and input

The room size is provided as input to the layout generator. In CoT Activation, we extract it from 3D-Front scene bounding boxes as part of the input in training. In DPO, the object size is inferred from the LLM during data pair generation, as shown in Appendix A.1. Specifically, the LLM predicts the appropriate size based on the scene description, and this inferred size is then used together with the scene context to construct data pairs for DPO training. Currently, we support rectangular rooms, which cover the majority of real-world layouts. Non-rectangular or complex-shaped rooms are an interesting future extension, though they are not the main focus of our method.

Q2: Handling complex scenes

In practice, our model performs well in scenes with up to 30 objects, which shows no significant difference compared to the baselines. This upper limit stems from the training data distribution (3D-Front rarely contains denser scenes), rather than algorithmic constraints. We believe the method is scalable, particularly through training on datasets where individual scenes contain more objects, as well as by incorporating strategies such as recursive layout decomposition, which divides complex rooms into manageable subregions. We are actively exploring these directions. We will add more discussions in the revised paper.

Q3: Object composition and substructures

In Figure 3 (last row), each item on the table is placed individually. Due to rebuttal formatting limits, we could not include more visualizations, but will provide extensive examples of such compositions in the next version to offer a more intuitive understanding of Iterative Asset-Layout Alignment process.

We hope our responses can address your concerns. Please feel free to inform us of any further issues.

Thank you sincerely for your valuable time and insightful comments.

W1 & Q4: Novelty of using LLM agent pipeline and CoT

Our primary contribution and key insight lie in presenting a new perspective specifically for the 3D scene synthesis domain. Prior approaches (e.g., ATISS) mainly rely on fully supervised training over limited datasets, which learn fixed spatial distributions and thus struggle to generalize to new object categories or diverse scenes. Recent efforts (e.g., Holodeck, I-Design) have begun leveraging large language models (LLMs) to address this gap, but these methods often depend on manually defined spatial constraints and external solvers, which limit their flexibility and ability to handle fine-grained spatial relations. It is widely recognized that LLMs find it challenging to directly generate precise numerical layouts. In contrast, our approach integrates CoT reasoning with DPO to significantly enhance the LLM’s generalizable spatial reasoning capabilities, enabling it to produce complete numerical 3D layouts directly. This opens new avenues for thinking about generative 3D scene layout design.

Moreover, for LLM agent, we use an entropy-weighted reward integration mechanism that dynamically balances multiple layout quality criteria. While most existing LLM research focuses on domains like mathematics or programming, 3D spatial reasoning is an important and challenging application scenario. Our work demonstrates a practical approach for enabling LLMs to tackle generative 3D spatial reasoning tasks. To facilitate further research, we plan to release the CoT dataset and source code upon paper acceptance.

W2 & Q3: Method section clarity and reproducibility

We acknowledge that the method section can be improved for clarity. We sincerely appreciate the valuable feedback and will incorporate these improvements in the subsequent version.

(1) On evaluation criteria definitions: The spatial and functional “criteria” mentioned (e.g., “reasonableness of spatial relations”, “accessibility and functionality”) serve as guiding directions for evaluators rather than rigidly defined quantitative metrics. These criteria are clarified with concrete prompt examples in Appendix A.1. We also provide more detailed explanations:

Relative Alignment (C1) This criterion assesses the geometric congruence and spatial relationship between related objects. It evaluates whether objects that are functionally or conceptually linked exhibit appropriate alignment, such as central, horizontal, or vertical congruity, or organized arrangements like grids, based on their intended spatial interaction.

Global Positioning (C2) This criterion evaluates the semantic appropriateness of an object's placement within the overarching room context. It assesses whether the object's position aligns with conventional spatial norms and functional requirements relative to the entire layout, ensuring it contributes to a coherent and functional environment.

Consistency with CoT (C3) This criterion verifies the fidelity of object placements against any explicit or inferred spatial instructions provided within a Chain-of-Thought (CoT) description. It ensures that the physical arrangement of objects in the scene directly corresponds to the textual guidance, identifying discrepancies where the visual representation deviates from the described intent.

Inter-object Distance (C4) This criterion measures the functional spacing between distinct objects, ensuring adequate separation for accessibility, navigability, and intended use. While minimal bounding box overlaps may be permissible in instances of plausible vertical stacking (e.g., an object placed on another), the primary assessment focuses on maintaining sufficient clearance for unimpeded interaction and movement.

To manage space in this rebuttal, we provide detailed explanations for four key criteria (C1–C4). If you’d like, we’d be happy to provide the full descriptions of all seven criteria in a follow-up response or the revised version.

(2) On formulas (3)-(7): These formulas are intended to clearly explain the detailed process of reward computation, aiming to enhance the clarity and reproducibility of the paper. However, we acknowledge that some formulas overlap with the textual descriptions. For example, Equation (3) can be more concisely described in words, and Equation (5) corresponds to entropy calculation, which can also be expressed textually. On the other hand, Equation (4) is difficult to describe clearly using only words, while Equations (6) and (7) could be combined but represent a complex weighting calculation that cannot be fully captured in simple language. We will streamline the paper by removing redundant overlaps between text and formulas, thereby improving the clarity and conciseness of the presentation.

(3) On reproducibility: All prompts and detailed experimental parameters are provided in the appendix. We will open source code and data upon acceptance to ensure full reproducibility.

W3: Inference time concerns

Our inference time, measured under consistent hardware and network conditions, covers the full pipeline from text input to 3D layout generation. As shown in the table (30 samples), our method performs competitively with baselines (Unit: seconds). Object generation (Trellis) takes ~26 seconds per object, but it's modular and can be replaced with object retrieval. As generation tools improve, this time is expected to drop. We don't consider this to be a fundamental bottleneck of our method.

W4: Handling of z-axis and true 3D layout

While BEV layouts represent objects as bounding boxes in the XY-plane, our framework allows overlapping bounding boxes to reflect vertical stacking. We leverage LLMs’ strong priors on object height ranges and relationships, enabling accurate height estimation despite the 2D base layout. As shown in Table 2, decomposing the problem (BEV layout + height reasoning) outperforms direct 3D generation attempts.

The issue in the bathroom_wo_refine example is not a general failure in height prediction, but a rare case of incorrect object perception, the model initially assumed the bathtub was filled with water, leading it to place the rubber duck on top. To address such cases, we introduce an Iterative Asset-Layout Alignment module that refines the layout based on feedback from generated assets, successfully correcting the error in later iterations. This is the result of the ablation experiment (not our full model) and our framework is specifically designed to handle this. Therefore, this case should not be considered a main weakness of our method, nor does the ablation visualization reflect a fundamental flaw.

Q1: CoT Reasoning Quality

To demonstrate the quality of reasoning, we provide below an example of a GPT-4o–generated CoT (only a partial example shown due to space constraints):

"Entity Extraction": "The scene contains: 2 multi-seat sofas, 1 armchair, 1 coffee table, 2 console tables, 1 cabinet, and 2 stools. These pieces suggest a living room setup focused on seating and moderate storage/display furniture.",

"Order Decision": "First, we place the two multi-seat sofas since they are the largest and central to the scene. Next, we position the coffee table in relation to the sofas to form a functional conversation area...",

"Spatial Reasoning": "The layout starts by defining the primary seating area. A multi-seat sofa is placed along the right side of the room, oriented vertically to establish one edge of the seating space. Its center is set around (162px, 176px), with dimensions of 60px in length and 25px in width, rotated -90 degrees. To complement this, a second multi-seat sofa is positioned horizontally below, forming an L-shaped arrangement. It is centered at (110px, 133px), maintaining a 0-degree orientation, creating a natural corner ideal for conversation..."

This example shows that GPT-4o produces structured and context-aware reasoning. The CoT is not superficial, it breaks down the task and considers spatial relationships rather than overfitting the final answer.

Q2: Raw Planner Output

Below is an example of raw planner output, showcasing its multi-step reasoning process (only a partial example shown due to space constraints):

"Entity Extraction": "The objects that should be placed in the scene include a multi-seat sofa, a lounge chair, a coffee table, a TV stand, a dining table, multiple dining chairs, and pendant lamps."

"Order Decision": "The order of placement should start with the largest and most central objects, which are the multi-seat sofa and the dining table, as they likely serve as the focal points of the living and dining areas..."

"Spatial Reasoning": "To begin shaping the room, a multi-seat sofa is positioned as the anchor of the seating area. It’s placed along the right side of the space, rotated vertically to make efficient use of the wall without cutting into circulation paths. With a generous width and length—measuring 79px by 34px—it defines the main visual boundary of the living zone. The center is set at (194px, 65px), maintaining openness in the central floor area..."

We hope our responses can address your concerns. Please feel free to inform us of any further issues.

Thank you once again for your thoughtful feedback and for taking the time to review our rebuttal. We would like to address the remaining concerns in your latest response.

1. On Novelty: Beyond Engineering Contributions We would like to re-emphasize that our contribution lies in proposing a new perspective for 3D indoor scene synthesis—a novel solution distinct from previous optimization-based methods (as explained in the previous rebuttal), and is not merely an engineering effort. Our work primarily addresses challenges in 3D indoor scene synthesis, not LLM agents. Therefore, more attention should be given to its contributions to the field of 3D indoor scene synthesis.

2. On Evaluation Criteria We appreciate your concern about the clarity and definition of the evaluation criteria. Our evaluators are implemented via reasoning LLM and VLM, not human, but they are instructed to mimic human judgment using semantically grounded criteria. While many tasks require rigid evaluation metrics, layout generation is inherently ambiguous and subjective. In our experience, using these criteria allows the evaluator model to reason in a human-like manner and yield consistent and practical judgments. For instance, in the case of inter-object distance: when a bed and nightstand heavily overlap in BEV, the evaluator uses physical priors to infer whether the overlap is likely due to vertical stacking. In this case, the evaluator would consider it unreasonable, as both objects are typically placed directly on the floor. We believe that overly rigid definitions may misclassify novel yet valid layouts. It is also difficult to rigidly define what constitutes a reasonable object placement, as it is nearly impossible to summarise or exhaustively enumerate all types of object interactions, spatial relationships, and functional requirements. That said, we agree that clearer documentation would help. As stated in our rebuttal, we have provide prompt examples in Appendix A.1 (this is exactly what we use in our practice, and it intuitively demonstrates how those evaluators are implemented). If you would find it helpful, we are happy to further provide: raw outputs from evaluators, or an example BEV layout to try it yourself, so you may directly assess how these judgments are made.

3. On the "Bathroom" Example and Role of Refinement Thank you for your observation on the bathroom_wo_refine case. We now provide a clearer explanation of the visual artifact: As shown in the supplementary figure, the bathtub asset includes a vertical shower fixture, which is considered part of the bathtub model. In the numeric layout, the rubber duck is placed with a z-range of 0.45–0.55, on top of the bathtub’s bounding box (z = 0–0.5). Since the visualizer renders the shower head and the duck at similar height ranges, the duck appears disproportionately high—even higher than nearby bowl. This is a side effect of not accounting for asset-specific geometry during initial layout generation. This issue arises not from a fundamental limitation in z-axis modeling, but from the inability of the layout planner to directly perceive asset geometry. Our refinement module addresses precisely this challenge, bridging the gap between symbolic layout reasoning and asset-level appearance grounding. To quantify the frequency of such issues and the effectiveness of refinement, we ran 20 test scenes:

• 13/20 scenes required no refinement
• 5/20 were corrected with 1 round of refinement
• 2/20 required 2 refinement rounds

These results indicate that while most scenes are resolved without refinement, the refinement module plays a crucial role in improving alignment when necessary. We will incorporate these findings more clearly in a revision.

Please let us know if you have any further questions.

Size proportion:

Sofa (79×34) vs Coffee table (49×49):
The coffee table is smaller in both dimensions than the sofa. This matches real-world expectations, where a sofa is a much larger focal object and the table sits in front of it. Yes.

TV stand (70×15) vs TV (70×15):
The TV and stand have identical bounding box dimensions. In reality, a stand should extend beyond the TV in at least one dimension to provide proper support and visual balance. Having them the same size means they fully overlap in BEV, which is structurally and visually unrealistic. No.

Dining table (75×34) vs Dining chairs (5×20):
The chairs are extremely short in one dimension (5px) compared to the table’s length (75px). Even if 20px represents the seat depth, a 5px width for the chair front-to-back footprint is too small relative to a table’s scale. This suggests a size mismatch—chairs should occupy more space in at least one dimension for comfortable seating. No.

Coffee table (49×49) vs Lounge chair (24×25):
The lounge chair is smaller in both width and depth compared to the coffee table, which is plausible. Lounge chairs typically occupy less floor space than a central coffee table. Yes.

Pendant lamps (26×26) over Coffee table (49×49) and Dining table (75×34):
In both cases, the lamp footprint is much smaller than the table area, which is correct for overhead lighting fixtures. Yes.

I sincerely appreciate the authors for their rebuttal.

• The concerns on LLM/CoT is partially addressed. This can be regarded as an (engineering) contribution to make it work, but novelty is still limited.
• About the clarity of the method part, the original text does not show that they are "guiding directions for (human) evaluators" - which can be guided by a "coarse direction", but are for automated evaluators - which do require a rigid definition. Also, the computation of the evaluation involves per-bject checking, which seems difficult to be performed by human. Even now, I am not sure how those evaluators are implemented.
• The concerns on the inference time are addressed.
• About the artifact in "bathroom_wo_refine", I am not evaluating it as it is from the full model, but considering the actual reason behind "why this requires refinement to prevent failure" and "whether this refinement is just a workaround for the fundamental limitation of lacking z-axis capability." I am also not convinced by the explanation of "the model initially assumed the bathtub was filled with water, leading it to place the rubber duck on top," because the duck is much higher than the bowl of the bathtub. I would like some further clarifications on how frequent this pattern and refiner works.
• The raw outputs of the LLM addresses my concerns on this. The GPT-4o's CoT is just what I expected, tries to generate a explanation that fits the answer; however, when a human actually plans, they may have a top-down or coarse-to-fine approach, or some revisions of exisiting plans when finding a later object cannot fit. This GPT CoT is just like a lucky person who magically puts all objects on all correct places without planning or retrying. I hope the actual model's CoT could be more powerful after RL training.

Thank you sincerely for your valuable time and insightful comments.

W1 & Q3: Lack of out-of-distribution (OOD) evaluation; impact of SFT/DPO on generalization

We appreciate the reviewer’s insightful concern. In fact, the qualitative and quantitative evaluations in both the paper and appendix already assess generalization to unseen scene types. During the Supervised Fine-Tuning (SFT) stage, we use data derived from 3D-Front, which only contains four scene categories: bedroom, living room, study, and dining room. However, the scene prompts used in DPO training and final evaluations are generated separately and explicitly exclude those scene types, ensuring no category-level overlap between training and evaluation. This separation was enforced via two disjoint prompt sets detailed in Appendix A.3.

W2: BEV limitation in handling vertical stacking (e.g., books on bookshelves)

We agree with the reviewer that BEV has inherent limitations in modeling vertical stacking. However, many such cases (e.g., “books on bookshelf”) can be treated as composite assets in our system. For example, “a filled bookshelf” can be generated as a single object, which captures the visual semantics without modeling each individual book.

Moreover, in our experiments, we find that explicitly separating BEV layout and 3D lifting leads to better overall performance than jointly modeling them, highlighting the effectiveness of our two-stage design. In practice, scenes with extremely dense vertical stacking (e.g., cluttered desktops or fully packed shelves) are relatively rare and not the primary focus of our work. That said, fully modeling fine-grained vertical clutter remains an important future direction, which may require more powerful spatial reasoning capabilities.

W3: No human study reported to directly assess alignment or plausibility

We thank the reviewer for pointing this out. In addition to automated evaluations (e.g., via LayoutVLM), we conduct a human evaluation study to directly assess alignment and plausibility from a user-centric perspective. Specifically, we recruite 25 compensated participants with relevant backgrounds in computer science and 3D vision. Each participant evaluated layout samples from multiple methods, randomized and anonymized, based on three axes (All ratings are collected using a 5-point Likert scale):

Semantic Compliance: Whether the layout reflects the input prompt faithfully.

Physical Plausibility: Whether objects are well-supported and physically reasonable.

Layout Rationality: Whether the spatial arrangement is typical and convincing.

Our method consistently received the highest average scores across all categories, showing strong agreement with human preferences. This study complements the VLM-based evaluation and confirms that our method produces layouts that are not only quantitatively strong but also preferred by human evaluators.

We also include comparison with ATISS, DiffuScene, and Infinigen:

While baselines perform strongly overall, our method matches or surpasses their performance on certain scene categories, and additionally supports open-vocabulary generation.

Q1: Robustness to noisy CoT traces

We conduct controlled experiments to assess robustness by:

• Shuffling the order of CoT steps, and
• Removing the CoT reasoning entirely.

Shuffling degrades performance modestly, while removing CoT causes a significant drop. These results show that structured CoT reasoning is essential for layout quality.

Q2: Importance of dual reward evaluators

Our reward signal integrates:

• VLM-based evaluator focuses on high-level visual and physical plausibility, judging the overall realism and global coherence of the generated layout.

• Reasoning LLM-based evaluator operates at a fine-grained, object-level, verifying that each object's position, orientation, and relation to others.

These components are complementary. Our ablations show that removing either leads to imbalanced layout optimization. As shown in Table 2, the full model using a dual-evaluator significantly outperforms the simple reward setting that relies on a single evaluator. Thus, the dual-evaluator setup is critical for robust and generalizable learning.

We hope our responses can address your concerns. Please feel free to inform us of any further issues.

Thank you sincerely for your valuable time and insightful comments.

W1 & Q1: Qualitative results

We provide multiple examples per granularity in the main paper and supplementary materials, though only one visualization per prompt is shown. Due to rebuttal formatting limits, more visuals cannot be included here. Instead, we present a quantitative study measuring the standard deviation of our model over ten repeated generations using the same prompt or scene type. Each table row shows results from one randomly sampled prompt under the given method.

As noted in the Appendix A.3, we generated 45 scenes per granularity. For I-Design that sometimes fails to produce valid results, we exclude those cases. Prompts in Figure 3 are randomly chosen from successful generations. We include 10+ randomly selected qualitative results in the paper and supplement, and will add more examples in a revision.

W2 & Q2: GPT-4o based evaluation is flawed; no human study

We adopt the GPT-based metric from LayoutVLM (CVPR 2025), which has been validated to align well with human judgments. While VLMs have limits in fine-grained spatial reasoning, we distinguish between generative spatial reasoning (precise 3D placement) and visual content understanding. The former requires exact spatial computation, the latter image perception. Therefore, properly calibrated VLM-based evaluation like LayoutVLM remains a valuable reference for layout quality.

In our pipeline, this metric is used only for evaluation, avoiding overfitting since training rewards come from detecting objective errors (e.g., collisions, CoT mismatch), which differ from the evaluation metric. We follow prior works like LayoutGPT and LayoutVLM using these to provide extra information. To further validate, we conduct a user study with 25 compensated volunteers experienced in computer science and 3D vision, who score layouts from various methods on three criteria: Semantic Compliance (alignment with textual description), Layout Rationality (plausibility of object placement), Physical Plausibility (realism of object support, no penetration, etc.).

Each method’s results were randomly ordered and anonymized. The average scores are reported below:

We also include comparison with ATISS, DiffuScene, and Infinigen:

While they perform strongly overall, our method matches or surpasses their performance on certain scene categories, and additionally supports open-vocabulary generation.

W3: Table 1 omits ATISS and DiffuScene

ATISS and DiffuScene are not directly comparable to our method due to fundamental differences in input and scope: 1. Our method supports open-vocabulary text inputs, while ATISS and DiffuScene are trained to generate specific scene types (e.g., bedrooms or living rooms). 2. Our model is LLM-based, while theirs are layout diffusion or scene synthesis networks that do not mainly rely on natural language inputs.

Despite this, to facilitate a fairer comparison, we randomly generate 20 samples for bedrooms and living rooms and evaluated across standard metrics:

W4 & Q3: Method cost

We discuss the cost here and will add more discussions in the revised paper.

Monetary cost: We don’t report per-scene monetary costs due to inconsistent baselines using different models (e.g., GPT-3.5, GPT-4, GPT-4o) with varying API pricing, which makes fair comparison difficult. To ensure transparency, we release a fully open-source pipeline that can run locally or on open LLMs, where cost-free variants outperform original baselines on layout quality. If needed, we can run experiments with a unified model (e.g., GPT-4o) to report average per-scene costs.

Inference time cost: We evaluate inference time systematically by measuring average, minimum, and maximum times for generating layouts from 10 random prompts at three granularity levels, all on the same platform and network. Except for LayoutGPT, which is limited to specific scene types, our method achieves better results with comparable inference times (Unit: seconds):

W5: Ambiguity in constraint-related distinction

We will clarify the details in the revised version.

Prior learning-based methods directly learn distributions of numerical layouts. Our method instead learns the logic behind object placement, which generalizes better across unseen scenes. For example, ATISS, LayoutGPT has difficulty generating scenes with open vocabulary.

Predefined constraints can theoretically generalize, but manually defining them is costly and often fails across diverse scene types. Prior work mainly uses coarse spatial relations like relative directions or distances. While LLMs handle such constraints more easily than generating exact layouts, this limits realism and diversity and struggles with fine-grained descriptions. For example, “the cake is placed at the top-left corner of the table” cannot be captured by simple rules like “A is left/right/above/below B” or “A is X meters from B.” In our method, such detailed spatial relations are treated as constraints generated by the LLM. User study show clear advantages in handling these fine-grained descriptions.

W6: Originality

Our novelty lies not in the use of CoT or DPO themselves, but in the new perspective they enable for 3D scene layout generation. Prior methods (e.g., ATISS) rely on fully supervised training over limited datasets, learning fixed spatial distributions that struggle to generalize to new categories or diverse scenes. Recent efforts (e.g., Holodeck, I-Design) have begun using LLMs to bridge this gap, but these typically depend on manually defined spatial constraints and external solvers, limiting flexibility and fine-grained spatial control. A common consensus regarding this type of work is that LLM finds it difficult to generate precise numerical layouts. In contrast, our method uses CoT and DPO to enhance generalizable reasoning, enabling LLMs to generate complete numerical layouts directly and opening up new ways of thinking.

We also introduce an entropy weight–based reward integration scheme to balance multiple layout quality aspects. While prior LLM research mainly targets math or coding, 3D spatial reasoning offers a valuable testbed. We provide a feasible method for LLM to handle generative 3D spatial reasoning problems. We will open source the CoT dataset and code to support further progress in this area.

W7: Ambitious Properties

We apologize for the wording that may have caused misunderstanding. While LLMs are not flawless, our work only leverages their commonsense reasoning for spatial relation generation, which is a narrower and more stable use case supported by prior work and empirical results. We will use more precise wording to reduce misunderstandings.

We hope our responses can address your concerns. Please feel free to inform us of any further issues.

You are asked to generate indoor scene descriptions at three levels of granularity: coarse, medium, and fine-grained. Please follow the instructions carefully.
There are three types of granularity:
1. Coarse: List the main objects in the room without mentioning where they are.  
   Example: "A home gym with a treadmill, yoga mat, dumbbell rack, water dispenser, and a large mirror."
2. Medium: Describe the approximate spatial relationships between major object groups.  
   Example: "In a playroom, a toy shelf stands against the right wall, a bean bag lies in the corner near the window, and a round play mat is placed in the center."
3. Fine-grained: Provide precise, detailed spatial arrangements among individual objects.  
   Example: "A small square table is placed in the center of the room. On the front right corner of the table sits a red toolbox, with a measuring tape coiled beside it. A yellow stool is tucked in on the left side, and a desk lamp stands at the rear center of the table."

You will generate scene descriptions for {num_scene_types} different indoor scene categories, excluding common categories such as bedroom, living room, dining room, and study.
For each scene category, generate:
{num_coarse_per_type} descriptions at coarse granularity,
{num_medium_per_type} at medium granularity,
{num_fine_per_type} at fine-grained granularity.
Each scene description should be accompanied by room dimensions: length, width, and height (each must be an integer <= 256).

Output the results in a strict json format as follows:
[
  {
    "scene_type": "laundry room",
    "granularity": "coarse",
    "description": "A laundry room with a washing machine, dryer, laundry basket, shelves, and detergent bottles.",
    "room_size": {
      "length": 256,
      "width": 171,
      "height": 240
    }
  },
  ...
]