H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields
ICLR 2024(2024)
Abstract
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.
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Key words
3D reconstruction,Neural implicit surface learning
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