  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.
