In this paper we propose a neural message passing approach to augment an
input 3D indoor scene with new objects matching their surroundings. Given an
input, potentially incomplete, 3D scene and a query location, our method
predicts a probability distribution over object types that fit well in that
location. Our distribution is predicted though passing learned messages in a
dense graph whose nodes represent objects in the input scene and edges
represent spatial and structural relationships. By weighting messages through
an attention mechanism, our method learns to focus on the most relevant
surrounding scene context to predict new scene objects. We found that our
method significantly outperforms state-of-the-art approaches in terms of
correctly predicting objects missing in a scene based on our experiments in the
SUNCG dataset. We also demonstrate other applications of our method, including
context-based 3D object recognition and iterative scene generation.

本文提出了一种神经传递信息的方法来与其环境相匹配的新物体进行增强，该方法能够预测适合位置的物体类型的概率分布，并在密集图中传递学习消息来处理物体相之间的空间和结构关系，通过注意机制加权消息，在 SUNCG 数据集中比其他方法更准确地预测场景中缺失的物体，并展示了基于此方法的其他应用，包括基于上下文的 3D 对象识别和迭代场景生成。