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arXiv · 2603.05888

PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction

Xiang Zhang·Sohyun Yoo·Hongrui Wu·Chuan Li·Jianwen Xie·Zhuowen Tu
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Abstract

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.

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Xiang Zhang, Sohyun Yoo, Hongrui Wu, Chuan Li, Jianwen Xie, Zhuowen Tu. 2026-03-06. PixARMesh: Autoregressive Mesh-Native Single-View Scene Reconstruction. https://arxiv.org/abs/2603.05888

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