FairScene: Learning unbiased object interactions for indoor scene synthesis

In this paper, we propose an unbiased graph neural network learning method called FairScene for indoor scene synthesis. Conventional methods directly apply graphical models to represent the correlation of objects for subsequent furniture insertion. However, due to the object category imbalance in dataset collection and complex object entanglement with implicit confounders, these methods usually generate significantly biased scenes. Moreover, the performance of these methods varies greatly for different indoor scenes. To address this, we propose a framework named FairScene which can fully exploit unbiased object interactions through causal reasoning, so that fair scene synthesis is achieved by calibrating the long-tailed category distribution and mitigating the confounder effects. Specifically, we remove the long-tailed object priors subtract the counterfactual prediction obtained from default input, and intervene in the input feature by cutting off the causal link to confounders based on the causal graph. Extensive experiments on the 3D-FRONT dataset show that our proposed method outperforms the state-of-the-art indoor scene generation methods and enhances vanilla models on a wide variety of vision tasks including scene completion and object recognition.