Recently, there has been an increased interest in NeRF methods which reconstruct differentiable representation of three-dimensional scenes. One of the main limitations of such methods is their inability to assess the confidence of the model in its predictions. In this paper, we propose a new neural network model for the formation of extended vector representations, called uSF, which allows the model to predict not only color and semantic label of each point, but also estimate the corresponding values of uncertainty. We show that with a small number of images available for training, a model quantifying uncertainty performs better than a model without such functionality. Code of the uSF approach is publicly available at https://github.com/sevashasla/usf/.

翻译：近年来，对能够重建三维场景可微表示的神经辐射场（NeRF）方法的兴趣日益增长。这类方法的主要局限性之一在于无法评估模型对其预测的置信度。本文提出一种用于生成扩展向量表示的新型神经网络模型——uSF，该模型不仅能预测每个点的颜色与语义标签，还能估算相应的不确定性数值。我们证明，在训练图像数量有限的条件下，具备不确定性量化功能的模型性能优于无此功能的模型。uSF方法的代码已在https://github.com/sevashasla/usf/ 公开。