NTrans-Net: A Multi-Scale Neutrosophic-Uncertainty Guided Transformer Network for Indoor Depth Completion

Dense depth maps are important constituents in a variety of tasks and have wide ranging applications. However, depth maps captured by indoor depth sensors have an extensive range of missing depth values and are also sparse in nature. Predicting dense depth from sparse input has been widely studied and is solved either as a regression or classification problem. In this paper, we propose a novel representation, termed Unified Ordinal Vectors, to realise the combined advantages of regression and classification methods. To disinter the potential of this representation, we propose NTrans-Net, a novel multi-scale network that can extract hierarchical and complementary information with neutrosophic indeterminacy feature handling. We also propose a dual encoder-decoder transformer structure to handle these neutrosophic domain features with guided attention to better capture inter-modal dependencies for superior depth completion performance. NTrans-Net is designed to be flexible enough to adapt to the dynamic nature of spatial input contexts and be robust to sensor-dependent distributions. We conduct extensive experiments on NYUv2 and ToF18K datasets to demonstrate the superiority of the proposed method in multiple settings, especially in realistic indoor environments as captured by commodity depth sensors.