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REVIEW 4 major objections 6 minor 60 references

Step-by-step, vision-checked reasoning produces better 3D indoor layouts than one-shot planning.

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2026-08-02 04:53 UTC pith:MAUCUEY6

load-bearing objection A solid progressive-reasoning system for 3D layout with a useful dataset and sensible RL, but the semantic claim is only partly established because GPT-4o is both reward and judge. the 4 major comments →

arxiv 2607.13539 v1 pith:MAUCUEY6 submitted 2026-07-15 cs.CV cs.GR

ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning

Yuan Xiao , Can Wang , Xiangyu Kong , Jing Liao This is my paper
classification cs.CV cs.GR
keywords 3D indoor scene generationprogressive reasoningvision-language modelschain-of-thoughtreinforcement learninginteractive layout editingscene rearrangementmulti-objective reward
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that 3D indoor scene generation should be treated as a progressive reason-then-act process: the model looks at the current partial room, explains where and why the next object should go, and places it before moving on. To train this behavior, the authors build 224,757 placement pairs from completed scenes by removing objects in size order, generate chain-of-thought rationales, and fine-tune a vision-language model on them. They then add a reinforcement-learning stage that separates rewards for hard physical constraints, soft semantic plausibility, and reasoning-action consistency, so the three objectives do not fight each other. The claimed result is that this pipeline beats one-shot and text-loop baselines on physical plausibility, semantic alignment, and localized editability, with the best average physically-grounded semantic alignment score of 51.8 on the benchmark. A generous reader would care because it suggests interactive editing no longer needs global re-optimization and because it weights “think first, then act” as a training signal for spatial tasks.

Core claim

At the paper's core is the claim that layout quality comes from decomposing synthesis into state-conditioned steps. At each step the model receives the user prompt, the current partial layout in JSON, a rendered view of the scene with coordinate grid marks, and the target object; it emits a <think> rationale and a structured <answer> from which the placement is parsed. This lets each decision be anchored to visual evidence and gives the model a way to recover from context, rather than committing to a full layout in one pass. On top of that, the paper introduces a tiered RL scheme that normalizes rewards separately for physical validity, semantic aesthetics, and reasoning-action grounding, ar

What carries the argument

The carrying mechanism is the progressive reason-then-act loop. It is trained on ThinkBLOX-Data-200K, a set of 224,757 procedural placement pairs built by peeling objects out of complete scenes (large to small) and pairing each step with multi-view images, a chain-of-thought rationale, and a JSON layout. The second mechanism is Tier-Decoupled GDPO, a policy update that normalizes rewards tier by tier: hard constraints (format, collision, boundary), soft semantics (functional alignment and a vision-language “yes” confidence), and reasoning-action consistency are each normalized within their own group before the advantages are weighted and combined. The loop supplies the reasoning step, and th

Load-bearing premise

The load-bearing premise is that the vision-language judge used for the semantic reward and the final semantic score is a valid, neutral measure of layout quality; because the same judge helps write the training rationales and scores the output, the semantic advantage could partly measure stylistic self-consistency rather than true scene quality.

What would settle it

Have a vision-language model that played no role in data creation, reward design, or evaluation score the same set of outputs, and compare its ranking with the human study; if the ThinkBLOX lead shrinks or flips when the training judge is not involved, the semantic gain is driven by alignment with the evaluator, not by a general improvement in scene quality.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • If the claims hold, local edits (add, move, replace, delete) modify only the target object or region, so an interactive user no longer triggers a full re-layout.
  • Physical plausibility and semantic coherence improve together rather than trading off, which one-shot planners typically force.
  • Dense, object-heavy scenes become tractable, since each placement is conditioned on the actual partial scene instead of predicted all at once.
  • The same inference loop covers global generation, global rearrangement, and local rearrangement, so one model serves several interactive tasks.
  • Tier-separated reward normalization generalizes as a recipe for other multi-objective generation tasks with conflicting criteria.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the reported semantic metric may partly reflect consistency with the same judge used for training and evaluation; a judge that never created any rationale or reward signal would tell whether the gain is general scene quality.
  • Beyond the paper: the order in which objects are placed is an implicit curriculum. Since the dataset peels scenes in size order, the model may inherit a large-anchor-first bias; training on multiple peel orders or searching over placement orders could improve robustness.
  • Beyond the paper: the reasoning-action consistency reward resembles grounding an agent's verbal plan to actual world coordinates, so the framework may transfer to embodied rearrangement tasks where a robot must verify each step against a visual state.
  • Beyond the paper: the measured improvements on collision-free and in-boundary scores suggest that explicit visual feedback (rendered current scene plus grid marks) could be a cheap add-on for existing one-shot systems, turning them into progressive refiners without full retraining.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper proposes ThinkBLOX, a VLM-based framework for 3D indoor scene generation that replaces one-shot layout prediction with a progressive, state-conditioned reason-then-act process. The authors construct ThinkBLOX-Data-200K, a dataset of 224,757 placement pairs with GPT-4o-generated CoT rationales and structured JSON layouts, perform SFT from Qwen2.5-VL-7B, and then apply a proposed Tier-Decoupled GDPO scheme with three reward tiers: physical validity, semantic aesthetics, and reasoning-action consistency. At inference, the model plans a global placement order and then places objects one by one, supporting local editing without global re-layout. The experiments compare against LayoutGPT, Holodeck, I-Design, and MetaSpatial on the LayoutVLM benchmark, reporting gains in CF/IB, positional/rotational coherency, and the PSA score, with additional cross-model and human evaluation. The abstract claims significant improvements in physical plausibility, semantic alignment, and interactive editability.

Significance. If the results hold, the work is significant: it addresses a real limitation of one-shot layout generators by making layout synthesis interactive and incremental, and it provides a large training dataset plus a reward-grouping scheme for multi-objective RL in a multimodal setting. The physical-plausibility claims are supported by objective geometric scores (CF/IB), and the ablations are extensive enough to show that the proposed components contribute. The cross-model and human studies are constructive attempts at robustness. However, the semantic half of the headline claim is currently not established because the training and evaluation loop overlaps on GPT-4o, the semantic metrics are not defined precisely, and the main comparison table lacks error bars and significance testing. The paper is therefore promising but needs substantial evidence before the central claim can be accepted.

major comments (4)
  1. [§III.A.2.b, §IV.c, §V.A.1] The semantic evaluation is partially closed-loop. GPT-4o generates the CoT training data (Sec. IV.c), GPT-4o's “Yes” log-probability is used as the dense semantic reward R_render = exp(log p_yes) during RL (Sec. III.A.2.b), and the PSA score is computed using a GPT-4o-based semantic rating (Sec. V.A.1). The reported PSA advantage (ThinkBLOX 51.8 vs MetaSpatial 38.6) may therefore partly measure alignment with the training-time judge rather than intrinsic layout quality. Table II and the human study are genuine mitigations, but Gemini 3 and Qwen3-VL belong to the same VLM family and the human study has only 33 participants with no reported significance tests. The paper should report semantic accuracy using a judge or metric that was not used for reward optimization, provide per-item correlation of R_render with human judgments, and state whether the PSA judge is identical to the reward ju
  2. [Table I] Table I reports CF, IB, Pos., Rot., and PSA without error bars or significance tests. The percentages appear in increments of 11.1, which implies roughly 9 evaluated scenes per room category. For example, the Living Room row shows CF=22.2 and PSA=9.9 for ThinkBLOX, which is inconsistent with the overall average and suggests high per-category variance. Because the paper claims consistency across room categories and significant overall improvement, the authors should report the number of scenes per category, confidence intervals, and paired statistical tests against each baseline. The ablation table (Table III) includes ± intervals, so there is no methodological reason to omit them from the main comparison.
  3. [§V.A.1] The semantic metrics Pos. Coherency and Rot. Coherency are never defined. The manuscript only states that they measure whether the layout aligns with the textual prompt, but it does not specify the algorithm, the thresholding, the evaluator, or whether the scores are continuous or binary. Since these numbers appear in Table I and Table III and are used to support the semantic-alignment claim, the lack of a precise definition prevents independent reproduction. The authors should provide the exact computation procedure or release the evaluation script.
  4. [Eq. (1), §III.A.2.c] Equation (1) performs a group-relative normalization per tier by dividing by std{r_k^(i,1..G)} with no additive epsilon. If a tier's reward is constant across the G rollouts—which can easily occur for S_cons when the parser always matches or for S_hard when all rollouts are collision-free and in-bounds—the advantage is undefined or infinite. The epsilon is only added in the later batch-wise normalization (Eq. (3)), not at the tier level. This is a stability issue in the core algorithmic contribution. Please add a guarded denominator and report the frequency of zero-variance tiers during training.
minor comments (6)
  1. [Abstract / Table I] The abstract says “11 room types,” but Table I appears to list 12 categories (Bedroom through Game Room). Please correct the count.
  2. [Table III] The rows labeled “SFT (one-shot) w/or n” and “SFT w/or n” are presumably typos for “w/o r_n” and “w/o r_n” given the section title “Ablating Progressive Reasoning.” Please fix the notation.
  3. [§IV.d] The quality-filtering criterion uses “Levenshtein similarity between the original and re-predicted answers,” but it does not say which model performs the re-prediction. Please specify the model and inference setting used for the consistency check.
  4. [§III.A.2.b] Please clarify the exact computation of R_render = exp(log p_yes): whether log p_yes is the raw logit, the log-softmax value, whether it is normalized by sequence length, and how cases with p_yes=0 or log p_yes=0 are handled. This matters for interpreting the reward scale and for reproducibility.
  5. [§IV.b / §III.B] The training dataset uses a size-based placement order, while inference uses a plan-based order. Please discuss whether this train-inference order mismatch is intentional and whether the model is robust to order changes beyond the ordering ablation shown in Table III.
  6. [Fig. 8 and §V.E] The human study text says “12 representative scenes” and “60 evaluated images,” while the figure caption says “12 room categories.” Please clarify whether the evaluation covered 12 scenes or 12 room categories; if the former, the claim that the advantage is stable across all 12 room categories is not supported.

Circularity Check

1 steps flagged

Semantic PSA gains are partially closed-loop: GPT-4o acts as both the RL reward and the PSA judge; cross-model and human checks mitigate but do not fully break the loop.

specific steps
  1. fitted input called prediction [Sec. III.A.2.b (Semantic Consistency reward) and Sec. V.A.1 (Evaluation Metrics)]
    "To obtain a dense semantic reward, we query GPT-4o on whether the generated layout conforms to the instruction and use the log-probability of the “Yes” token: R render = e logprobyes, where Rrender ∈ [0,1] reflects the evaluator’s confidence. ... The PSA score is computed by integrating the GPT-4o-based semantic rating, where GPT-4o serves as a human-aligned evaluator [37], with the physical plausibility metric."

    R_render is directly optimized during Tier-Decoupled GDPO: the policy is rewarded for increasing GPT-4o's “Yes” log-probability on the rendered layout. The headline semantic metric, PSA, is computed from the same GPT-4o semantic rating. Thus the reported semantic advantage (e.g., ThinkBLOX 51.8 vs MetaSpatial 38.6 PSA) is partly a measure of how well the policy fits the GPT-4o judge used as its own training signal, not an independent property of layout quality. The paper's cross-model judges (Gemini 3, Qwen3-VL-30B) and 33-participant human study are genuine mitigations, but the cross-model judges are still VLM judges, and the human study is small and reports no significance tests. The physical CF/IB metrics are geometric and independent, so the circularity burden is confined to the semant

full rationale

The physical-plausibility part of the claim rests on self-contained geometric metrics: CF and IB are rule-based and do not depend on the model being evaluated. The central semantic claim, however, is partially closed-loop. In training, R_render = exp(log p_yes) rewards the policy for eliciting GPT-4o's “Yes” on the rendered layout (Sec. III.A.2.b). In evaluation, the PSA score integrates GPT-4o's semantic rating (Sec. V.A.1). Consequently, a portion of the PSA gain over baselines that were never trained against this GPT-4o reward is forced by construction: the policy is fitted to the same judge that later scores it. This is compounded by the dataset construction, where the CoT rationales used for SFT are also generated by GPT-4o, further aligning the model to GPT-4o's preferences. The paper's evaluator-robustness experiment with two other VLM judges and the human study provide partial independent evidence, preventing a fully circular verdict. Nevertheless, because the primary automatic semantic metric is the same function as the training reward, the semantic half of the headline claim is not fully independent; an intermediate circularity score is warranted.

Axiom & Free-Parameter Ledger

6 free parameters · 6 axioms · 0 invented entities

No new physical or conceptual entities are postulated. The contributions — Tier-Decoupled GDPO, tiered rewards, and the reasoning-action consistency objective — are defined explicitly in Eqs. 1–3 and the reward section. GPT-4o functions as an external oracle rather than an entity invented by this paper. The main ledger weight falls on free parameters (λ, unreported tier weights w_k, β, G, curation thresholds) and on domain assumptions about GPT-4o validity and greedy sequential placement.

free parameters (6)
  • λ (collision gain) = 5.0
    Scales R_coll = −tanh(λ C_coll) in Sec. III.A.2.a; chosen by hand, directly shapes the physical reward.
  • Tier weights w_k (Eq. 2) = not reported
    Weights for hard/soft/cons advantage terms; the paper defines the weighted sum but never reports the weight values — a gap for both method and reproducibility.
  • KL coefficient β = 0.004
    Sec. V.A.3; controls deviation from the SFT policy during GDPO.
  • Rollout group size G = 8
    Number of sampled rollouts per prompt in Eq. 1; set in Sec. V.A.3.
  • Dataset curation thresholds = ≥5 objects; ≥3 reasoning steps; Levenshtein similarity > 0.8
    Sec. IV.a and IV.d; hand-chosen filters that determine the training distribution and the quality gate for GPT-4o CoT.
  • Training hyperparameters = LoRA rank 16, alpha 32, LR 2e-5, SFT 10K steps, GDPO 2K steps
    Sec. V.A.3; standard but hand-chosen settings.
axioms (6)
  • domain assumption GPT-4o judgments are a valid, human-aligned measure of layout semantic quality
    Invoked as R_render in Sec. III.A.2.b and as the PSA/semantic evaluator in Sec. V.A.1; failure here invalidates the semantic-alignment half of the central claim.
  • domain assumption Greedy sequential placement conditioned on rendered partial scenes suffices for globally coherent layouts
    Core paradigm of Sec. III; the paper's own Living Room results (CF 22.2, PSA 9.9, Table I) suggest it fails on some dense categories.
  • domain assumption GDPO is a stable value-model-free RL base and tier decoupling repairs its reward conflict
    Sec. III.A.2; relies on [11]; ablation shows plain GDPO at PSA 47.3 vs tier-decoupled 54.6, so most of the RL benefit is credited to the tiering.
  • domain assumption Large-to-small object ordering captures functionally correct placement dependencies
    Used in dataset construction (Sec. IV.b, Algorithm 1) and inference planning (Sec. III.B); misordered anchors would teach wrong placement priors.
  • domain assumption Parse() via template-guided regex faithfully recovers placements from free-form CoT
    R_cons = −dist(Parse(r_n), a_n) in Sec. III.A.2.c; malformed or verbose reasoning traces would corrupt the grounding reward.
  • domain assumption GPT-4o rationales passing textual-similarity filtering are correct placement explanations
    Sec. IV.d: the Levenshtein >0.8 filter measures textual repetition between original and re-predicted answers, not geometric or semantic correctness.

pith-pipeline@v1.3.0-alltime-deepseek · 22343 in / 24220 out tokens · 235827 ms · 2026-08-02T04:53:39.472667+00:00 · methodology

0 comments
read the original abstract

While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based generators predominantly adopt a one-shot paradigm where the full layout is planned at once. This one-shot approach often requires global re-optimization or complete reconstruction during interactive editing (e.g., inserting or moving objects) and can lead to physically or semantically poorly organized arrangements. To address these challenges, we propose ThinkBLOX, a VLM-based progressive reasoning framework that iteratively designs and refines 3D scenes. ThinkBLOX treats layout generation as a state-conditioned, step-by-step reasoningand-action process. To power this, we construct the ThinkBLOX-Data-200K dataset, containing 224,757 procedural placement pairs annotated with multi-view scene context, explicit Chain-of-Thought (CoT) rationales, and structured JSON layouts. Through supervised fine-tuning (SFT) on this dataset, the VLM learns to bridge the reasoning-action gap under incremental updates. Furthermore, recognizing that scene synthesis is inherently a multisolution task where SFT suffers from reward conflict, we introduce Tier-Decoupled GDPO. This reinforcement learning scheme organizes heterogeneous rewards into distinct tiers, stabilizing policy optimization across physical validity, semantic plausibility, and reasoning-action consistency. Extensive experiments show that ThinkBLOX significantly outperforms recent one-shot and iterative baselines in physical plausibility, semantic alignment, and interactive editability. Additionally, we show that it supports diverse applications, including both global and local generation and rearrangement of 3D scenes.

Figures

Figures reproduced from arXiv: 2607.13539 by Can Wang, Jing Liao, Xiangyu Kong, Yuan Xiao.

Figure 1
Figure 1. Figure 1: Overall comparison between one-shot and progressive generation. (a) One-shot synthesis directly generates all object placements at once, which limits [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of ThinkBLOX framework. Training: We curate ThinkBLOX-Data-200K with multi-view context, CoT rationales, and JSON layouts, and train Qwen2.5-VL by SFT and Tier-Decoupled GDPO with tiered rewards (Shard, Ssoft, Scons). Inference: Given a text prompt p, ThinkBLOX plans the placement order and progressively generates or edits layout L through a CoT-guided reason-then-act process rn, supporting global… view at source ↗
Figure 3
Figure 3. Figure 3: Construction Pipeline of ThinkBLOX-Data-200K. Starting from an indoor scene [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison on the benchmark. We compare with baseline methods in generating layouts based on detailed prompts. Our method is able [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on ThinkBLOX-Data-200K. We report representative results on test dataset across diverse room types. Compared to baselines, [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Local layout rearrangement comparison. Given an existing scene and an editing prompt, we compare add/move/replace edits across methods (orange [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Effect of inference ordering. Plan-based ordering yields clearer functional zones and better organization (yellow/orange), while random and size-based [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average human evaluation results across 12 room categories. [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 1
Figure 1. Figure 1: Construction Pipeline of ThinkBLOX-Data-200K. Starting from an indoor scene [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Representative visualizations of ThinkBLOX-Data-200K. In the final [PITH_FULL_IMAGE:figures/full_fig_p015_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative ablation results. Our full ThinkBLOX model employs progressive reasoning, Tier-Decoupled GDPO, a complete reward suite, visual representations, and a plan-based inference ordering strategy. Removing progressive reasoning rn, downgrading the policy optimization algorithms, omitting specific reward signal sets, removing visual marks, or altering the inference ordering strategy consistently degrad… view at source ↗
Figure 4
Figure 4. Figure 4: More qualitative comparison on the benchmark. We compare with baseline methods in generating layouts based on detailed prompts. Our method is [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional qualitative examples of local layout rearrangement under [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: More examples of fine-grained local layout editing. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of five methods across 12 room categories under three evaluation metrics. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗

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Reference graph

Works this paper leans on

60 extracted references · 9 linked inside Pith

  1. [1]

    arXiv preprint arXiv:2510.12095 , year=

    Il3d: A large-scale indoor layout dataset for llm-driven 3d scene generation , author=. arXiv preprint arXiv:2510.12095 , year=

    arXiv

  2. [2]

    Liu, Shih-Yang and Dong, Xin and Lu, Ximing and Diao, Shizhe and Belcak, Peter and Liu, Mingjie and Chen, Min-Hung and Yin, Hongxu and Wang, Yu-Chiang Frank and Cheng, Kwang-Ting and others , journal=

  3. [3]

    IEEE Transactions on Visualization and Computer Graphics , year=

    Chat2Layout: Interactive 3D furniture layout with a multimodal LLM , author=. IEEE Transactions on Visualization and Computer Graphics , year=

  4. [4]

    arXiv preprint arXiv:2406.03866 , year=

    Llplace: The 3d indoor scene layout generation and editing via large language model , author=. arXiv preprint arXiv:2406.03866 , year=

    Pith/arXiv arXiv

  5. [5]

    Advances in Neural Information Processing Systems , volume=

    Layoutgpt: Compositional visual planning and generation with large language models , author=. Advances in Neural Information Processing Systems , volume=

  6. [6]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    Layoutvlm: Differentiable optimization of 3d layout via vision-language models , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  7. [7]

    MetaSpatial: Reinforcing 3D Spatial Reasoning in

    Zhenyu Pan and Han Liu , booktitle=. MetaSpatial: Reinforcing 3D Spatial Reasoning in

  8. [8]

    European Conference on Computer Vision , pages=

    Anyhome: Open-vocabulary generation of structured and textured 3d homes , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  9. [9]

    Advances in Neural Information Processing Systems , volume=

    3d-llm: Injecting the 3d world into large language models , author=. Advances in Neural Information Processing Systems , volume=

  10. [10]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    3d-grand: A million-scale dataset for 3d-llms with better grounding and less hallucination , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  11. [11]

    Scene-LLM: Extending Language Model for 3D Visual Reasoning , year=

    Fu, Rao and Liu, Jingyu and Chen, Xilun and Nie, Yixin and Xiong, Wenhan , booktitle=. Scene-LLM: Extending Language Model for 3D Visual Reasoning , year=

  12. [12]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Ll3da: Visual interactive instruction tuning for omni-3d understanding reasoning and planning , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  13. [13]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Gpt4point: A unified framework for point-language understanding and generation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  14. [14]

    European Conference on Computer Vision , pages=

    Pointllm: Empowering large language models to understand point clouds , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  15. [15]

    Advances in Neural Information Processing Systems , volume=

    Chat-scene: Bridging 3d scene and large language models with object identifiers , author=. Advances in Neural Information Processing Systems , volume=

  16. [16]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Spatialvlm: Endowing vision-language models with spatial reasoning capabilities , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  17. [17]

    Advances in Neural Information Processing Systems , volume=

    Spatialrgpt: Grounded spatial reasoning in vision-language models , author=. Advances in Neural Information Processing Systems , volume=

  18. [18]

    European Conference on Computer Vision , pages=

    Scenescript: Reconstructing scenes with an autoregressive structured language model , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  19. [19]

    European Conference on Computer Vision , pages=

    Agent3d-zero: An agent for zero-shot 3d understanding , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  20. [20]

    arXiv preprint arXiv:2310.11441 , year=

    Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v , author=. arXiv preprint arXiv:2310.11441 , year=

    Pith/arXiv arXiv

  21. [21]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Vip-llava: Making large multimodal models understand arbitrary visual prompts , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  22. [22]

    Proceedings of the 31st International Conference on Computational Linguistics , pages=

    Scaffolding coordinates to promote vision-language coordination in large multi-modal models , author=. Proceedings of the 31st International Conference on Computational Linguistics , pages=

  23. [23]

    2025 , doi =

    Guo, Daya and others , journal =. 2025 , doi =

    2025

  24. [24]

    DeepSeek-R1 , author=

    Comparative evaluation of advanced AI reasoning models in pediatric clinical decision support: ChatGPT O1 vs. DeepSeek-R1 , author=. MedRxiv , pages=. 2025 , publisher=

    2025

  25. [25]

    arXiv preprint arXiv:2204.05862 , year=

    Training a helpful and harmless assistant with reinforcement learning from human feedback , author=. arXiv preprint arXiv:2204.05862 , year=

    Pith/arXiv arXiv

  26. [26]

    IEEE/CAA Journal of Automatica Sinica , volume=

    A brief overview of ChatGPT: The history, status quo and potential future development , author=. IEEE/CAA Journal of Automatica Sinica , volume=. 2023 , publisher=

    2023

  27. [27]

    arXiv preprint arXiv:2412.16720 , year=

    Openai o1 system card , author=. arXiv preprint arXiv:2412.16720 , year=

    Pith/arXiv arXiv

  28. [28]

    Liu, Jiawei and Zhang, Lingming , howpublished =

  29. [29]

    arXiv preprint arXiv:2504.20073 , year=

    Ragen: Understanding self-evolution in llm agents via multi-turn reinforcement learning , author=. arXiv preprint arXiv:2504.20073 , year=

    Pith/arXiv arXiv

  30. [30]

    ACM Transactions on Graphics (TOG) , volume=

    Example-based synthesis of 3D object arrangements , author=. ACM Transactions on Graphics (TOG) , volume=. 2012 , publisher=

    2012

  31. [31]

    2021 International conference on 3D vision (3DV) , pages=

    Sceneformer: Indoor scene generation with transformers , author=. 2021 International conference on 3D vision (3DV) , pages=. 2021 , organization=

    2021

  32. [32]

    Advances in neural information processing systems , volume=

    Atiss: Autoregressive transformers for indoor scene synthesis , author=. Advances in neural information processing systems , volume=

  33. [33]

    2025 , url =

    Qwen Team , journal =. 2025 , url =

    2025

  34. [34]

    arXiv preprint arXiv:1505.06289 , year=

    Text to 3d scene generation with rich lexical grounding , author=. arXiv preprint arXiv:1505.06289 , year=

    Pith/arXiv arXiv

  35. [35]

    Proceedings of the 2014 conference on empirical methods in natural language processing , pages=

    Learning spatial knowledge for text to 3D scene generation , author=. Proceedings of the 2014 conference on empirical methods in natural language processing , pages=

    2014

  36. [36]

    ACM Transactions on Graphics , volume=

    Grains: Generative recursive autoencoders for indoor scenes , author=. ACM Transactions on Graphics , volume=

  37. [37]

    Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

    Graph-to-3d: End-to-end generation and manipulation of 3d scenes using scene graphs , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision , pages=

  38. [38]

    arXiv preprint arXiv:2508.17832 , year=

    HLG: Comprehensive 3D Room Construction via Hierarchical Layout Generation , author=. arXiv preprint arXiv:2508.17832 , year=

    Pith/arXiv arXiv

  39. [39]

    2026 International Conference on 3D Vision (3DV) , pages=

    Hsm: Hierarchical scene motifs for multi-scale indoor scene generation , author=. 2026 International Conference on 3D Vision (3DV) , pages=. 2026 , organization=

    2026

  40. [40]

    ACM Transactions on Graphics (TOG) , volume=

    Planit: Planning and instantiating indoor scenes with relation graph and spatial prior networks , author=. ACM Transactions on Graphics (TOG) , volume=. 2019 , publisher=

    2019

  41. [41]

    and Ritchie, Daniel , title =

    Wang, Kai and Savva, Manolis and Chang, Angel X. and Ritchie, Daniel , title =. ACM Trans. Graph. , month = jul, articleno =. 2018 , issue_date =

    2018

  42. [42]

    Advances in Neural Information Processing Systems , volume=

    Commonscenes: Generating commonsense 3d indoor scenes with scene graph diffusion , author=. Advances in Neural Information Processing Systems , volume=

  43. [43]

    International Journal of Computer Vision , volume=

    3d-future: 3d furniture shape with texture , author=. International Journal of Computer Vision , volume=

  44. [44]

    European Conference on Computer Vision , pages=

    Sceneteller: Language-to-3d scene generation , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  45. [45]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Holodeck: Language guided generation of 3d embodied ai environments , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  46. [46]

    Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment , volume=

    Scenecraft: Automating interactive narrative scene generation in digital games with large language models , author=. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment , volume=

  47. [47]

    2024 , booktitle =

    Zhou, Xiaoyu and Ran, Xingjian and Xiong, Yajiao and He, Jinlin and Lin, Zhiwei and Wang, Yongtao and Sun, Deqing and Yang, Ming-Hsuan , title =. 2024 , booktitle =

    2024

  48. [48]

    2024 , booktitle =

    Wang, Yufei and Xian, Zhou and Chen, Feng and Wang, Tsun-Hsuan and Wang, Yian and Fragkiadaki, Katerina and Erickson, Zackory and Held, David and Gan, Chuang , title =. 2024 , booktitle =

    2024

  49. [49]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    SceneX: Procedural Controllable Large-Scale Scene Generation , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  50. [50]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Habitat synthetic scenes dataset (hssd-200): An analysis of 3d scene scale and realism tradeoffs for objectgoal navigation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  51. [51]

    Proceedings of the Twelfth International Conference on Learning Representations , year=

    InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior , author=. Proceedings of the Twelfth International Conference on Learning Representations , year=

  52. [52]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Gpt-4v (ision) is a human-aligned evaluator for text-to-3d generation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  53. [53]

    arXiv preprint arXiv:2410.21276 , year=

    Gpt-4o system card , author=. arXiv preprint arXiv:2410.21276 , year=

    Pith/arXiv arXiv

  54. [54]

    The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

    Direct Numerical Layout Generation for 3D Indoor Scene Synthesis via Spatial Reasoning , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

  55. [55]

    European Conference on Computer Vision , pages=

    I-design: Personalized llm interior designer , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  56. [56]

    European Conference on Computer Vision , pages=

    Forest2seq: Revitalizing order prior for sequential indoor scene synthesis , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  57. [57]

    Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

    CasaGPT: cuboid arrangement and scene assembly for interior design , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

  58. [58]

    2025 , eprint=

    Qwen2.5-VL Technical Report , author=. 2025 , eprint=

    2025

  59. [59]

    arXiv preprint arXiv:2402.03300 , year=

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models , author=. arXiv preprint arXiv:2402.03300 , year=

    Pith/arXiv arXiv

  60. [60]

    2024 , eprint=

    LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models , author=. 2024 , eprint=

    2024