Regularizing neural semantic fields for 3D semantic understanding of scenes with sparse input views

In the past couple of years, Neural Radiance Fields (NeRF) [1] and methods derived from it have become a popular choice for solving different tasks such as novel view synthesis, 3D reconstruction, generative modeling, and semantic segmentation. Both 3D reconstruction and semantic segmentation are key tasks for different geospatial applications, e.g. creating maps using unmanned aerial vehicles (UAV) and photogrammetry. The main difference between such a geospatial use case and a typical NeRF application is the density/sparsity of the input views, i.e. most mapping applications are done over large geographical extents resulting to scenes or subscenes with sparse input views. This MSc topic will design and implement a method based on NeRF for joint 3D reconstruction and semantic segmentation from sparse input views.