SceneReVis: 一种基于多轮强化学习的视觉 grounding 框架，用于通过多轮强化学习进行 3D 室内场景合成
SceneReVis: A Self-Reflective Vision-Grounded Framework for 3D Indoor Scene Synthesis via Multi-turn RL
AI总结：
SceneReVis 通过多轮强化学习和多模态反馈，实现高保真 3D 室内场景合成，提升空间规划能力。
摘要：
当前单次通过的 3D 场景合成方法常常由于缺乏深入推理而出现空间幻觉，如碰撞。为弥补这一差距，我们引入了 SceneReVis，一种基于视觉的自我反思框架，它采用迭代的“诊断-行动”循环，利用多模态反馈显式拦截并解决空间冲突。为了支持这种逐步的范式，我们构建了 SceneChain-12k，一个通过新颖的逆向工程管道生成的大型因果构造轨迹数据集。我们进一步提出了一种两阶段训练配方，从监督微调过渡到代理强化学习，使模型转变为活跃的空间规划器。广泛的实验表明，SceneReVis 在高保真生成和目标导向优化方面实现了最先进的性能，并在长尾领域表现出鲁棒的泛化能力。
英文摘要：
Current one-pass 3D scene synthesis methods often suffer from spatial hallucinations, such as collisions, due to a lack of deliberative reasoning. To bridge this gap, we introduce SceneReVis, a vision-grounded self-reflection framework that employs an iterative ``diagnose-and-act'' loop to explicitly intercept and resolve spatial conflicts using multi-modal feedback. To support this step-wise paradigm, we construct SceneChain-12k, a large-scale dataset of causal construction trajectories derived through a novel reverse engineering pipeline. We further propose a two-stage training recipe that transitions from Supervised Fine-Tuning to Agentic Reinforcement Learning, evolving the model into an active spatial planner. Extensive experiments demonstrate that SceneReVis achieves state-of-the-art performance in high-fidelity generation and goal-oriented optimization, with robust generalization to long-tail domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
