We present LRM-Zero, a Large Reconstruction Model (LRM) trained entirely on
synthesized 3D data, achieving high-quality sparse-view 3D reconstruction. The
core of LRM-Zero is our procedural 3D dataset, Zeroverse, which is
automatically synthesized from simple primitive shapes with random texturing
and augmentations (e.g., height fields, boolean differences, and wireframes).
Unlike previous 3D datasets (e.g., Objaverse) which are often captured or
crafted by humans to approximate real 3D data, Zeroverse completely ignores
realistic global semantics but is rich in complex geometric and texture details
that are locally similar to or even more intricate than real objects. We
demonstrate that our LRM-Zero, trained with our fully synthesized Zeroverse,
can achieve high visual quality in the reconstruction of real-world objects,
competitive with models trained on Objaverse. We also analyze several critical
design choices of Zeroverse that contribute to LRM-Zero's capability and
training stability. Our work demonstrates that 3D reconstruction, one of the
core tasks in 3D vision, can potentially be addressed without the semantics of
real-world objects. The Zeroverse's procedural synthesis code and interactive
visualization are available at: this https URL

我们提出了 LRM-Zero，它是一个完全基于合成 3D 数据进行训练的大型重建模型，可以实现高质量的稀疏视角 3D 重建。LRM-Zero 的核心是我们的程序化 3D 数据集 Zeroverse，它是通过简单的基本形状进行自动合成，并进行随机纹理和增强处理（例如，高度场、布尔差异和线框）。与前期的 3D 数据集（例如 Objaverse）不同，Zeroverse 完全忽略了现实全局语义，但在复杂的几何和纹理细节上与真实物体局部相似甚至更加复杂。我们证明了我们的 LRM-Zero，在使用我们完全合成的 Zeroverse 进行训练时，可以在重建真实世界对象方面达到与使用 Objaverse 训练的模型竞争性的高视觉质量。我们还分析了对于 LRM-Zero 的能力和训练稳定性有贡献的 Zeroverse 的几个关键设计选择。我们的工作表明，作为 3D 视觉的核心任务之一，可能可以不考虑真实世界物体的语义来解决 3D 重建问题。Zeroverse 的程序化合成代码和交互式可视化可在此 URL 中获取。