uSF: Learning Neural Semantic Field with Uncertainty
Vsevolod Skorokhodov Darya Drozdova Dmitry Yudin
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/.