  Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance
Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D
indoor scene reconstruction. We introduce a novel two-phase learning approach,
H2O-SDF, that discriminates between object and non-object regions within indoor
environments. This method achieves a nuanced balance, carefully preserving the
geometric integrity of room layouts while also capturing intricate surface
details of specific objects. A cornerstone of our two-phase learning framework
is the introduction of the Object Surface Field (OSF), a novel concept designed
to mitigate the persistent vanishing gradient problem that has previously
hindered the capture of high-frequency details in other methods. Our proposed
approach is validated through several experiments that include ablation
studies.
