Traditional image-to-3D models often struggle with scenes containing multiple
objects due to biases and occlusion complexities. To address this challenge, we
present REPARO, a novel approach for compositional 3D asset generation from
single images. REPARO employs a two-step process: first, it extracts individual
objects from the scene and reconstructs their 3D meshes using off-the-shelf
image-to-3D models; then, it optimizes the layout of these meshes through
differentiable rendering techniques, ensuring coherent scene composition. By
integrating optimal transport-based long-range appearance loss term and
high-level semantic loss term in the differentiable rendering, REPARO can
effectively recover the layout of 3D assets. The proposed method can
significantly enhance object independence, detail accuracy, and overall scene
coherence. Extensive evaluation of multi-object scenes demonstrates that our
REPARO offers a comprehensive approach to address the complexities of
multi-object 3D scene generation from single images.

REPARO 是一种从单个图像生成多个物体的三维模型的新方法，通过提取物体、重构三维网格、优化布局以及使用可微渲染技术，它能够有效地恢复三维资产的布局，达到高度的独立性、精确性和整体场景的一致性，从而解决了从单个图像生成多个物体的三维场景的复杂性问题。