We introduce PixARMesh, a method to autoregressively reconstruct complete 3D indoor scene meshes directly from a single RGB image. Unlike prior methods that rely on implicit signed distance fields and post-hoc layout optimization, PixARMesh jointly predicts object layout and geometry within a unified model, producing coherent and artist-ready meshes in a single forward pass. Building on recent advances in mesh generative models, we augment a point-cloud encoder with pixel-aligned image features and global scene context via cross-attention, enabling accurate spatial reasoning from a single image. Scenes are generated autoregressively from a unified token stream containing context, pose, and mesh, yielding compact meshes with high-fidelity geometry. Experiments on synthetic and real-world datasets show that PixARMesh achieves state-of-the-art reconstruction quality while producing lightweight, high-quality meshes ready for downstream applications.

翻译：本文提出PixARMesh方法，能够从单张RGB图像自回归地重建完整的三维室内场景网格。与现有依赖隐式符号距离场和事后布局优化的方法不同，PixARMesh在统一模型中联合预测物体布局与几何结构，通过单次前向传播即可生成连贯且可直接用于艺术创作的网格。基于近期网格生成模型的进展，我们通过跨注意力机制将像素对齐的图像特征与全局场景上下文融入点云编码器，从而实现了从单张图像进行精确空间推理。场景通过包含上下文、姿态和网格信息的统一令牌流进行自回归生成，最终产生具有高保真几何结构的紧凑网格。在合成数据集和真实数据集上的实验表明，PixARMesh在保持最先进重建质量的同时，能够生成适用于下游应用的轻量化高质量网格。