Compositional 3D scene synthesis has diverse applications across a spectrum
of industries such as robotics, films, and video games, as it closely mirrors
the complexity of real-world multi-object environments. Early works typically
employ shape retrieval based frameworks which naturally suffer from limited
shape diversity. Recent progresses have been made in shape generation with
powerful generative models, such as diffusion models, which increases the shape
fidelity. However, these approaches separately treat 3D shape generation and
layout generation. The synthesized scenes are usually hampered by layout
collision, which implies that the scene-level fidelity is still under-explored.
In this paper, we aim at generating realistic and reasonable 3D scenes from
scene graph. To enrich the representation capability of the given scene graph
inputs, large language model is utilized to explicitly aggregate the global
graph features with local relationship features. With a unified graph
convolution network (GCN), graph features are extracted from scene graphs
updated via joint layout-shape distribution. During scene generation, an
IoU-based regularization loss is introduced to constrain the predicted 3D
layouts. Benchmarked on the SG-FRONT dataset, our method achieves better 3D
scene synthesis, especially in terms of scene-level fidelity. The source code
will be released after publication.

从场景图生成逼真合理的三维场景，利用大型语言模型显式汇总全局图特征和局部关系特征，通过统一的图卷积网络从更新的场景图中提取图特征，并引入基于 IoU 的正则化损失来约束预测的三维布局。在 SG-FRONT 数据集上进行基准测试，我们的方法在场景级准确性方面取得了更好的三维场景合成效果。发表后将发布源代码。