RoomCraft：可控且完整的3D室内场景生成
RoomCraft: Controllable and Complete 3D Indoor Scene Generation
AI总结：
提出RoomCraft多阶段流水线，结合约束驱动优化框架与冲突感知定位策略，将多模态输入转化为连贯完整的3D室内场景，显著优于现有方法。
摘要：
从用户输入生成逼真的3D室内场景仍然是计算机视觉和图形学中一个具有挑战性的问题，需要仔细平衡几何一致性、空间关系和视觉真实感。神经生成方法由于全局空间推理能力有限，常常产生重复元素；而程序化方法虽然可以利用约束进行可控生成，但在多约束场景下却面临困难。当约束变得众多时，经常发生物体碰撞，迫使移除家具物品，从而破坏布局完整性。为了解决这些局限性，我们提出了RoomCraft，这是一个多阶段流水线，可将真实图像、草图或文本描述转换为连贯的3D室内场景。我们的方法将场景生成流水线与约束驱动的优化框架相结合。该流水线首先从用户输入中提取高层场景信息，并将其组织成包含房间类型、家具物品和空间关系的结构化格式。然后，它构建一个空间关系网络来表示家具布置，并使用基于启发式的深度优先搜索（HDFS）算法生成优化的放置序列，以确保布局连贯性。为了处理复杂的多约束场景，我们引入了一种统一的约束表示法，可处理形式化规范和自然语言输入，通过全面的动作空间设计实现灵活的面向约束的调整。此外，我们提出了一种冲突感知定位策略（CAPS），动态调整放置权重以最小化家具碰撞并确保布局完整性。大量实验表明，RoomCraft在跨多种输入模态生成逼真、语义连贯且视觉上吸引人的房间布局方面显著优于现有方法。
英文摘要：
Generating realistic 3D indoor scenes from user inputs remains a challenging problem in computer vision and graphics, requiring careful balance of geometric consistency, spatial relationships, and visual realism. While neural generation methods often produce repetitive elements due to limited global spatial reasoning, procedural approaches can leverage constraints for controllable generation but struggle with multi-constraint scenarios. When constraints become numerous, object collisions frequently occur, forcing the removal of furniture items and compromising layout completeness. To address these limitations, we propose RoomCraft, a multi-stage pipeline that converts real images, sketches, or text descriptions into coherent 3D indoor scenes. Our approach combines a scene generation pipeline with a constraint-driven optimization framework. The pipeline first extracts high-level scene information from user inputs and organizes it into a structured format containing room type, furniture items, and spatial relations. It then constructs a spatial relationship network to represent furniture arrangements and generates an optimized placement sequence using a heuristic-based depth-first search (HDFS) algorithm to ensure layout coherence. To handle complex multi-constraint scenarios, we introduce a unified constraint representation that processes both formal specifications and natural language inputs, enabling flexible constraint-oriented adjustments through a comprehensive action space design. Additionally, we propose a Conflict-Aware Positioning Strategy (CAPS) that dynamically adjusts placement weights to minimize furniture collisions and ensure layout completeness. Extensive experiments demonstrate that RoomCraft significantly outperforms existing methods in generating realistic, semantically coherent, and visually appealing room layouts across diverse input modalities.
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。
